land use and land cover mapping in the brazilian amazon using polarimetric airborne p-band sar data

2956 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 46, NO. 10, OCTOBER 2008

Land Use and Land Cover Mapping in theBrazilian Amazon Using Polarimetric

Airborne P-Band SAR DataCorina da Costa Freitas, Luciana de Souza Soler, Sidnei João Siqueira Sant’Anna, Luciano Vieira Dutra,

João Roberto dos Santos, José Claudio Mura, and Antônio Henrique Correia

Abstract—In September 2000, an airborne synthetic apertureradar (SAR) mission acquired unprecedented full polarimetricP-band data over the Tapajós National Forest (Pará State), whichis an area in the Brazilian Amazon which has been continuouslymonitored in the last three decades. Eight land use/cover classeswere identified, namely, primary forest, regeneration older than25 years, regeneration between 12 and 25 years, regenerationbetween 6 and 12 years, regeneration younger than six years,crops/pasture, bare soil, and floodplain (FP). The objective of thispaper is to analyze the potential of full polarimetric P-band datain distinguishing different land use/cover classes with a minimumestablished Kappa value of 75%, using the latest development onSAR statistical characterization. The iterated conditional mode(ICM) contextual classifier was applied to amplitude, intensity im-ages, biomass index, and some polarimetric parameters (entropy,α angle, and anisotropy) extracted from the polarimetric P-banddata. As the accuracy obtained for eight classes was not accept-able, another two sets, with five and four classes, were formedby the combination of the previous ones. They were defined byconfusion matrix analysis and by the graphical analysis of averagebackscatter values, entropy, α angle, and anisotropy images andby the H/α plans of the land use samples. The classificationaccuracy with four classes (three levels of biomass plus FP) wasthen considered acceptable with a Kappa value of 76.81%, usingthe ICM classification with the adequate bivariate distribution forthe HV and VV channels.

Index Terms—Image classification, radar polarimetry, syntheticaperture radar (SAR), terrain mapping.

Manuscript received October 2, 2007; revised April 5, 2008. Current versionpublished October 1, 2008. This work was supported in part by CNPq underProjects 305546/2003-1, 304274/2005-4, 305411/2006-3, and 381630/2000-5and in part by FAPEMIG under Grant 070/04.

C. da Costa Freitas, L. V. Dutra, and J. C. Mura are with the Image ProcessingDivision, National Institute for Space Research, São José dos Campos 12227-010, Brazil (e-mail: [email protected]).

L. de Souza Soler is with Wageningen University, 6700 AA Wageningen,The Netherlands, and also with the National Institute for Space Research, SãoJosé dos Campos 12227-010, Brazil (e-mail: [email protected]).

S. J. S. Sant’Anna is with the Image Processing Division, National Institutefor Space Research, São José dos Campos 12227-010, Brazil, and also withthe Technological Institute of Aeronautics, São José dos Campos 12228-900,Brazil.

J. R. dos Santos is with the Remote Sensing Division, National Institute forSpace Research, São José dos Campos 12227-010, Brazil.

A. H. Correia is with the National Institute for Space Research, São José dosCampos 12227-010, Brazil.

Digital Object Identifier 10.1109/TGRS.2008.2000630

I. INTRODUCTION

D IFFERENT vegetation types of forest physiognomy oc-cupy approximately 76% of the 5 million km2 of the

Brazilian Amazon [1]. Deforestation is one of the mainproblems affecting the region, with a rate of approximately14.0 km2/year. Recent data show that the conversion of forestareas by slash-and-burn practices, following agricultural andpasture activities, rose to 679.9 km2/year in this region [2].However, in 2002, 19% of the total amount of deforestedareas was estimated to be abandoned in a process of naturalrecovery [3]. Forest patches in different stages of secondarysuccession can be found all over the Amazon [4], [5]. De-forestation caused significant environmental damage, such ashabitat fragmentation, loss of biodiversity, and decrease of soilfertility [6]–[8]. The adequate mapping of this huge regionrequires remote sensing data and suitable techniques to supportdecision makers on environmental issues, such as forest inven-tory, land use practices, and deforestation control, in the nearfuture. Additionally, such data sets and maps can be used as in-puts for carbon emission/reabsorption estimates resulting fromlarge-scale land use/cover changes that affect global climateanalysis [9].

Optical remote sensing is used in the official methodology ofdeforestation assessment in Brazil, which makes it difficult toestimate forest clearings under clouds that continuously coversome parts of the Amazon. Due to its ability to acquire imagesthrough clouds, synthetic aperture radar (SAR) data were testedas an alternative to optical data [1], [10]–[15] to map changesof land use/land cover, to estimate biophysical parameters oftropical vegetation types, and to detect deforestation. Besidesits usefulness for imaging the Earth’s surface in areas with astrong cloud cover, microwave sensors deliver a huge amountof information contained in the radar response.

In particular, the low-frequency SAR (L- and P-bands) is ap-plicable to tropical regions because of its capability to penetratethe forest structure. L-band data are most interesting for theuser community because of their availability either on orbital orairborne platforms [16]–[18]. Although P-band orbital missionsare planned to produce aboveground biomass maps at a globalscale [19], P-band data are, up to this time, only available onairborne platforms due to restrictions on frequency allocation.In spite of orbital limitations, P-band airborne data presentedsignificant results in forested areas [20]–[22]; however, manyissues on the use of P-band imagery still need to be addressed

0196-2892/$25.00 © 2008 IEEE

FREITAS et al.: LAND USE AND LAND COVER MAPPING IN THE BRAZILIAN AMAZON 2957

and understood, such as low-frequency radar interaction withdifferent vegetation types and its statistical behavior.

Hoekman and Quiñones [18], [23] presented results usingAIRSAR data (C-, L-, and P-bands) for land cover classi-fication using backscatter in decibels, phase difference, andcorrelation modeled by Gaussian, circular Gaussian, and Betadistributions, respectively. Land use and land cover classifica-tion using multifrequency SAR P-band data are also addressedin this paper, considering a number of other possible distribu-tions from single to full polarimetric data, where most of themare recently derived.

It is demonstrated that P-band backscatter from the differenttypes of land use/land cover are better modeled when one con-siders different statistical distributions, leading to more preciseclassification maps. A special version of an iterated conditionalmode (ICM) contextual algorithm is considered in this paperbecause it allows one to use different a priori distributions to theadjustment of radar data [14], [24]–[26]. Additionally, it is thefirst time that P-band full polarimetric data are acquired inthe Brazilian Amazon, which is quite a different environmentthan the other South American test sites. Taking these consider-ations into account, the main goal of this paper is to analyze thepotential of the full polarimetric P-band data in distinguishingthe different stages of secondary succession and the differenttypes of land cover in the Brazilian tropical environment. Theknowledge of these vegetation cover types is essential not onlyfor mapping purposes but also for assisting forest inventory,which is an input to biodiversity assessments and carbon cyclemodeling studies. Amplitude, intensity images, a biomass index(BMI), and some polarimetric parameters (entropy, α angle,and anisotropy) were extracted from the polarimetric P-banddata and classified by a contextual supervised classificationprocedure and by individual and combinations of the polari-metric bands. The minimum classification accuracy establishedfor this paper is a Kappa coefficient of agreement of 75%,which, according to Landis and Koch [27], is considered avery good classification result. This paper includes a theo-retical approach applied to microwave remote sensing datacharacterization based on the multiplicative model and showsthat an adequate statistical characterization can improve theclassification results.

II. AREA UNDER STUDY AND DATA DESCRIPTION

The area under study includes part of the Tapajós Na-tional Forest (Pará State) and is located within the coordinates54◦49′36′′–55◦01′45′′ WGr and 02◦56′38′′–03◦23′38′′ S. Theradar images used in this paper cover an area of approximately27 km2, which is located along the BR-167 Cuiabá–Santarémhighway.

The region presents a yearly rainfall varying from 1750 to2000 mm, with a dry period of 40 days. The relative moisturereaches its highest levels in February (above 90%). Most ofthe area is covered by dense forest with deep soils. Secondarysuccession areas at several stages and agriculture and cattle-raising areas [28] are found.

The P-band SAR images used in this paper were taken onSeptember 2000 during an imaging mission in the Tapajós

National Forest, using an airborne SAR system developed byAeroSensing Radarsysteme GmbH [29]. The original imageswere set in a slant range, with a pixel spacing of 1.5 m × 0.67 m,which is at one look to all polarimetric channels in the linearbasis, namely, HH, HV, VH, and VV.

At least ten years of annual Landsat Thematic Mapper (TM)images were also available for visual analysis, which, togetherwith field information, were used as a support to extract samplesfor classification procedures. A field survey was conducted inparallel with the SAR campaign to characterize the physiog-nomic and structural aspects of primary and secondary forests,whose methodological procedure of forest inventory and re-sults were presented by [1]. Additionally, during field survey,land cover identification was performed as a reference fortraining and testing procedures. All samples were adequatelygeoreferenced.

Based on field information, eight classes were defined,namely, primary forest (PF), very old (VO) regeneration (olderthan 25 years), old regeneration (OR, between 12 and 25 years),intermediate regeneration (IR, between 6 and 12 years), newregeneration (NR, less than six years), crops/pasture (CP), baresoil (BS), and floodplain (FP). The PF presents approximately360 trees/ha distributed in four layers, forming an irregularcanopy with a variable angular distribution of twigs and leaves.The trees were measured in the diameter class of 10–20 cmcorresponding to 57% of the total individuals, presenting anaverage height of 14 m for this Diameter at Breast Height(DBH) interval. Dominant trees that compose the upper stratumpresent DBH mean values of 42 cm and a height above 25 m;the aboveground biomass is approximately 220 ton/ha. In thearea under study, there are few VO regeneration sections,which contain about 670 trees/ha, with average values of 18 cmand 14 m for DBH and height, respectively, but presenting,in the upper stratum, dominant trees composing the canopyof the stand, with a height of 22 m. The biomass can reachup to 150 ton/ha. The main occurring species are Bagassaguianensis (Moraceae), Guatteria poeppigiana (Annonaceae),Apeiba albiflora (Tiliacea), Cassia pentandra (Fabaceae), andCouratari oblongifolia (Lecythidaceae), which make up around55% of the floristic composition identified. OR, with approx-imately 88 ton/ha of aboveground biomass, has an averageheight of 11 m and DBH of 11 cm, with 105 species identified,such as Duguetia spixana (Anonaceae), Bagassa guianensis(Moraceae), Cariana sp. (Lecythidaceae), Inga falcistipula(Leguminosae Mimosoideae), V. guianensis (Guttiferae), andCochlospermum orinocense (Cochlospermaceae), representing43% of the total trees measured [1]. Areas defined as NRhave a mean height of 6 m and DBH of 5.8 cm, with anaerial biomass of around 10 ton/ha. Floristic compositionis dominated by species such as Vismia cayennensis (Gut-tiferae), Trema micrantha (Ulmaceae), Cecropia leucocoma(Moraceae), Cecropia scyadophylla (Moraceae), Chrysophyl-lum prieurei (Sapotaceae), Duguetia spixana (Anonaceae), andCordia bicolor (Boraginaceae), which represent 63% of thetotal individuals measured out of the 64 species identified [1].IR has an average height of 8 m and DBH of 7.7 cm, withan aerial biomass of about 28 ton/ha. Floristic composition isthe same as NR, with the addition of other species, including


Bagassa guianensis (Moraceae), Inga falcistipula (Legumi-nosae Mimosoideae), and V. guianensis (Guttiferae). Thesespecies represent 58% of a total of 121 species identified [1].The CP areas contain mainly corn, pepper, and rice cultures andclean/overgrown pastures. Areas defined as BS present fallowground and ploughed soil.

According to Vieira et al. [30], the rates of natural regrowthmay vary across regions due to differences in edaphic, climate,and historical land use factors; thus, most relationship is sitespecific among properties and forest age. Succession status,as the characterization by species composition, biomass, anddistributions of height and diameters, may be superior to standage as a procedure to stratify these forests for the characteri-zation of remote sensing (spectral) properties. In this paper, theregeneration classes were each defined by primarily referring toits age due to the observation of this parameter during the fieldsurvey and to the convenience of relating it to the Landsat TMarchives. Specifically for this data set, the relationship betweenthe P-band data and the biomass is discussed in [1].

The FP class presented the highest pixel values, and a simplethresholding classification produced a mask for distinguishingthis class from the others.

III. METHODS FOR CLASSIFICATION OF SAR DATA

This section describes the statistical properties of the SARdata, the contextual supervised classification, and the classifi-cation procedures.

A. Statistical Properties of SAR Data

For a reciprocal medium, the three unique elements of thescattering matrix define a complex vector [31]

Z = [SHH

√2SHV SVV ]T (1)

where T denotes the transpose operator. The√

2 is includedto ensure consistency in the span (total power) computation(see [32]).

The vector Z represents one-look polarimetric data. Usually,as it is the case of the data used in this paper, the multilookdata are considered, and in order to derive their distributionalproperties, the vector Z in (1) is thus considered to be the kthsingle-look observation and is denoted by Z(k). A fixed number

n of independent outcomes of Z are averaged to form the n-look 3 × 3 covariance matrix, which is given by [33]

Z(n) =1n

n∑k=1

Z(k)Z∗(k)T (2)

where Z∗(k)T denotes the transposed conjugate of Z(k). Thestatistical properties of Z(n) can be derived on the basis ofthe multiplicative model, which states that the data obtainedwith coherent illumination are the outcome of the productof two independent random variables, with one modeling theterrain backscatter and the other one modeling the specklenoise. Assuming that the speckle obeys a multivariate complexGaussian law [34], different distributions can be derived forZ(n), depending on the distributional properties assumed for theterrain backscatter. Three types of distributions are consideredin this paper for the terrain intensity backscatter, namely, a con-stant, the Gamma distribution, and the inverse of the Gammadistribution. These are conditional density frequency modelsproposed for terrain with no texture, fine texture, and coarsetexture [24]. These models lead, respectively, to the followingdistributions for the covariance matrix Z(n): Wishart [35], mul-tivariate K [36], and multivariate G0 [26], [37]. These densitiesare given, respectively, by (3)–(5), shown at the bottom ofthe page, where q denotes the number of components; Tr( )denotes the trace of a matrix; Γ( ) is the Gamma function;K(n, q) = π(1/2)q(q−1)Γ(n) · · ·Γ(n − q + 1); C = E[ZZ∗T],where E[ ] denotes the expected value of a random variable; andα, in (4) and (5), represents the parameters of the distributions.The distributions described by these three equations reduce,respectively, to the Gamma (Γ), intensity K (KI), and intensityG0 (G0

I) when q = 1 [24].Lee et al. [36], [38], [39] derived, from (3), some distribu-

tions that do not necessarily consider the full polarimetric infor-mation. One of these distributions is the bivariate distribution oftwo intensity data.

Denoting the pair of intensities as R1, R2, their joint densityis given by

p(R1, R2) =nn+1(R1R2)

n−12 exp

[−

n(

R1H11

+R2

H22

)1−|ρc|2

](H11H22)

n+12 Γ(n) (1 − |ρc|2) |ρc|n−1

× In−1

[2n|ρc|

1 − |ρc|2

√R1R2

H11H22

](6)

fZ(n)(z) =nqn|z|(n−q) exp

[−nTr(C−1z)

]K(n, q)|C|n , n, q > 0 (3)

fZ(n)(z) =2|z|(n−q)(nα)(α+qn)/2Kα−qn

(2√

nαTr(C−1z))

K(n, q)|C|nΓ(α)Tr(C−1z)(qn−α)/2, α, n, q > 0 (4)

fZ(n)(z) =nqn|z|(n−q)Γ(qn − α)

(−α)αΓ(−α)K(n, q)|C|n(nTr(C−1z) − α

)qn−α , −α, n, q > 0 (5)


where H11 = E[R1], H22 = E[R2], In−1 denotes the modifiedBessel function of order n − 1, and

ρc =E [S1S

∗2]√

E [|S1|2] E [|S2|2]= |ρc|eiθ. (7)

Knowing that the square of the magnitude of the complexcorrelation coefficient (|ρc|2) is equal to the intensity correla-tion coefficient [36], the parameter |ρc|2 can be estimated byselecting a sample of size m and computing

r̂ =

m∑i=1

[(R1i − R1)(R2i − R2)

]√

m∑i=1

[(R1i − R1)2

] m∑i=1

[(R2i − R2)2

] (8)

where R̄1 and R̄2 denote the sample means of R1 and R2,respectively.

B. Context, the Potts–Strauss Model, and the ICM Algorithm

The use of Markovian distributions (also known as Markovrandom fields) for the parametric modeling of context datesback to the 70s, but its use became widespread after thework by Geman and Geman [40]. Markov random fields area multidimensional extension of the index of Markov chains,where the concept of future given is transformed into spatialconditioning. The interest in this kind of distributions datesback to the beginning of the century, the well-known Isingmodel for magnetism being one of its most famous particularcase. The reader is referred to [41] for more information aboutits use in image analysis. A particular case of a Markov randomfield is the Potts–Strauss model, which is one of the mostsuccessful models used for describing the a priori distributionof the classes. Let a map have k ≥ 2 classes and be defined onthe grid G, where every position p ∈ G has a well-defined setof neighbors ∂p ⊂ G \ {p}, such that q ∈ ∂p ⇔ p ∈ ∂q. Then,following the notation given in [42], the Potts–Strauss model isgiven by the set of conditional probabilities

ln Pr (Ωp = ξ|Ωpc = ωpc) ∝ η#{q ∈ ∂p : ωq = ξ} (9)

where pc denotes the complement of p and #A represents thenumber of elements of the set A. Each random variable Ωp takesvalues in the set of possible classes.

Once the Potts–Strauss model is defined, it can be used asa prior probability for the classes in a Bayesian framework.For the discussion of the possible ways to obtain estimators fordata given for the true map, the reader is referred to [41]. Oneof these estimation techniques is the ICM. The ICM estimatorunder the Potts–Strauss model is the result of an iterative

algorithm that, starting from an arbitrary initial solution, im-proves it by updating each coordinate with the class ξ̂ given by

ξ̂ = arg maxξ

[ln fz(zp | ξ) + η#{ωq = ξ : q ∈ ∂p}] (10)

and continues until some stopping criterion is satisfied. It canbe seen that the function (10) to be maximized combinesboth radiometric and contextual information. The balance be-tween these two sources of information is provided by theparameter η; when η = 0, it reduces to maximum likelihood(ML), whereas for η → ∞, the rule disregards radiometricinformation and becomes the mode of the observed classesin the neighborhood. This parameter is estimated from theavailable information, i.e., from the previous classification.It is natural to start from a configuration without any priorinformation, such as, for instance, a collection of observationsof independent identically distributed random variables that areuniformly distributed on the classes. With such configuration,the first estimated η will be close to zero, and therefore, thenext classification will be very close to ML. In order to savecomputational time, we always start from the ML classification,avoiding, therefore, the influence of the initialization on theperformance of the ICM algorithm.

The classification system used in this paper was built asan extension of the Environment for Visualizing of Imagesplatform, and it uses the ICM algorithm. The algorithm stopswhen a certain percentage of classified pixels changes from oneiteration to the next or when a number of complete iterationswas performed. These parameters (percentage and number ofiteration) are defined by the user. In this paper, the classifica-tions were performed, considering 5% and eight iterations asstopping conditions. More details about the system and someapplications for JERS-1 (L-band) and SIR-C (L- and C-bands)can be seen in [26], [42], [43], and [44].

The system permits the classifications of individual or acombination of amplitude/intensity images, full polarimetricimages [using the covariance matrix described in (2), which ismodeled with (3)–(5)], a pair of intensities [modeled with (6)],the difference of phase images, the ratio of intensities, and apair of intensity-difference phase images. The last three werenot used in this paper, and therefore, the densities associatedwith them were not presented here. The reason for not usingthe phase difference information for the classification was that aprevious analysis of the images showed that they did not containrelevant information for the data set and the classes consideredin this paper.

An improved ML classification procedure was implementedin this system because it permits, among several possibledistributions, the one that is best adjusted for the data foreach class. In order to decide which distribution will beused, a χ2 goodness of fit test is performed for each class,which is based on training samples. In the case of individualamplitude images, the square root of Gamma, K-amplitude,G-amplitude, log-normal, and Gaussian distributions weretested for each class. In the case of full polarimetric classifi-cation, the multivariate distributions used to fit the covariancematrix are the Wishart, multivariate K, and multivariate G,which are given by equations (3)–(5), respectively. The decision


about which multivariate distribution will be used is made byfitting individual intensity images by Gamma, KI , and GI andby using the multivariate distribution corresponding to the onewhich is the majority of the individual cases.

Both classifications (ML and ICM) are supervised and thusrequire the specification of training sets for parameter estima-tion. These sets are informed by regions of interest, which arepreviously defined by the user. The equivalent number of looks[n in (3)–6, and (8)] is also an input parameter, and it is esti-mated only once for the whole image by taking samples fromhomogeneous (no texture) areas in each amplitude image. A χ2

goodness of fit test for the square root of Gamma distribution isperformed, and the individual n’s are estimated from the samplemoments for those areas that had a proper fit. The average ofthese individual n’s is considered as the final estimate of n.

The parameters for all the densities mentioned before are alsoestimated by the system. The description of its estimators canbe found in [24], [26], and [39].

C. Preclassification Procedures

As mentioned before, the radar data provided by AeroSens-ing Radarsysteme GmbH is one-look slant range full polarimet-ric complex data (complex HH, HV, VH, and VV). The antennapattern correction was performed by using a function based onhomogeneous extended areas (PF).

The polarimetric complex data were calibrated by using themethod of Quegan [45] supported by corner reflectors deployedin the area to determine the channel imbalance. After calibra-tion, the channel imbalance presented a ratio of 0.99 for theintensity and −0.05 rad for the phase, which are measured inthe corner reflectors; the crosstalk parameters presented valuesthat are below −33 dB. Based on these values, the data wereconsidered well calibrated.

Considering that the radar system operates in the monostaticmode, HV and VH differ only due to the noise. Because of this,we use the complex average of both channels, which is calledHV in this paper, in order to increase the signal-to-noise ratio.

The 3 × 3 multilook covariance matrix (2) was then formedby averaging 2 × 5 (range × azimuth) pixels, making the pixelapproximately square. Three elements of this matrix are inten-sity data, and the remaining ones are complex data. Besidesbuilding the images of the covariance matrix, other images werealso formed and were used as input for the system in orderto provide different classifications, namely, three multilookamplitude images (HH, VV, and HV), a BMI, an entropy image(H), an anisotropy image (A), and an alpha image (α).

The amplitude images were formed by taking the square rootof the multilook intensity images. Following Pope et al. [46],the BMI is defined as the average of the HH and VV amplitudeimages. According to these authors, the BMI for the P-band isan indicator of the relative amount of woody compared withleafy biomass. Therefore, it is expected that BS and CP wouldhave lower values of BMI than secondary and PF. The H/A/αimages were derived from the target polarimetric decomposi-tion theorem proposed in [47] and [48]. They were obtainedfrom the eigenvalues and eigenvectors of the average coherencematrix which is related to physical scattering mechanisms. The

H/α plane (where α is the average scattering mechanism) canbe used for classification purposes [49], [50].

D. Classification Procedures

After forming the images mentioned before, the followingML/ICM classifications were performed with the eight classesdefined in Section II.

1) Classification of individual amplitude images HH, VV,and HV using different distributions for each class, whichis given by (3)–(5), with q = 1 for the amplitude data,resulting in three different classifications. The objectiveof these classifications was to separately analyze thepotential of each channel for discrimination purposes.

2) Classification of the pair of intensities (HH–VV, HH–HV,and HV–VV). These classifications were performed byusing the bivariate distribution given by (6)–(8).

3) Classification of the set of the three intensitiesHH–HV–VV. This classification was performed by usinga Gaussian multivariate distribution.

4) Classification of the covariance matrix (full polarimetricclassification). This classification uses the multivariatedistributions given by (3)–(5) to describe the covariancematrix data. Each class may be fitted by different distri-butions. Some results of the full polarimetric classifica-tion were presented in [14] and [42].

5) Classification of the BMI image. The BMI values for eachclass were also fitted by the same distributions mentionedearlier for the amplitude data.

6) Classification of individual images obtained by entropy,α angle, and anisotropy using Gaussian distributionsresulting in three different classifications. The objectiveof these classifications was to separately analyze thepotential of each parameter for discrimination purposes.

7) Classification of the set of entropy, α angle, andanisotropy based on a Gaussian multivariate distribution.

8) Classification of the three intensities HH–HV–VV joinedto the entropy, α angle, and anisotropy images. Thisclassification was performed by using the Gaussian mul-tivariate distribution.

Based on the analysis of these 14 classifications, a classmerging procedure (explained in Section IV) was performed,considering the misclassification among them, as measured ontest samples. Table I shows the number of pixels for trainingand test samples for each class. After defining two sets of fiveand four classes, the same classifications listed before wereperformed with these new sets. It is important to notice that thenumber of points per class in the test samples was taken with theleast difference possible (maximum of difference observed—336 pixels) in order to avoid bias toward the particular accuracyof one or more classes. For illustration purposes, Fig. 1 showsthe intensity HH, HV, and VV data, and Fig. 2 shows thecolor composition of these three channels showing the trainingand test samples for all defined classes. As a reference, Fig. 3shows a piece of a georeferenced Landsat-7/TM image fromAugust 2000.

The criterion used to compare the classification resultswith varying number of classes was the Kappa coefficient of


TABLE ICLASS MERGING FROM EIGHT TO FIVE AND FOUR CLASSES WITH THE NUMBER OF PIXELS FOR

TRAINING AND TEST SAMPLES, WITH ACRONYMS USED FOR EACH CLASS

Fig. 1. P-band intensity data. (a) HH channel. (b) HV channel. (c) VV channel.

Fig. 2. Color composition of P-band intensity data (red, HH; green, HV; and blue, VV) with the training (solid polygons) and test (hachured polygons) samplesfor all classes considered.


Fig. 3. Color composition of Landsat-7/TM image from August 2000 (red, 5; green, 4; and blue, 3).

agreement (κ̂) [51], [52], which compensates for the chance(a priori) classification accuracy of each class. The Kappa co-efficient definition includes the estimation of the a priori prob-abilities of classes based on the number of training or testexamples, which is given by (11). Kappa and their correspond-ing variances (σ2

κ̂), which are given by (12), were computedfor all classifications, which are based on confusion matrices,by [51]

κ̂ =θ1 − θ2

1 − θ2(11)

σ2κ̂ =

1N

[θ1(1 − θ1)(1 − θ2)2

+2(1 − θ1)(2θ1θ2 − θ3)

(1 − θ2)3

+(1 − θ1)2

(θ4 − 4θ2

2

)(1 − θ2)4

](12)

where

θ1 =r∑

i=1

xii

N

θ2 =r∑

i=1

xi+ x+i

N2

θ3 =r∑

i=1

xii(xi+ + x+i)N2

θ4 =r∑

i=1

r∑j=1

xij(xj+ + x+i)N3

where r is the total number of classes in the confusion matrix,xii is the ith principal diagonal element of the confusion matrix,xi+(x+i) is the total of line (column) i, and N is the totalnumber of observations. The final step included the analysis ofall the classification results with the help of two-sided statisticaltests of equality of Kappa for all the pairs of classifications.

IV. RESULTS

The equivalent number of looks (n) was estimated from 21samples from homogeneous areas (adequately fitted by a squareroot of Gamma distribution) for each amplitude data—HH,HV, and VV. The final estimate of n was computed as theaverage of those individual estimates. It is interesting to noticethat about 90% of these samples were areas from PF and old

secondary succession. This might be an indication that theseclasses are homogeneous for the P-band frequency, consideringthat for constant terrain backscatter (no texture), the observeddistribution is basically the speckle noise, i.e., a square root ofGamma for the amplitude data. The estimated values for eachpolarization were 3.417 (HH), 4.565 (HV), and 4.146 (VV),given 4.043 as the final estimate of n.

The distributions that best fitted the data for the individualchannels HH, HV, and VV were log normal and GA for mostof the classes. The log-normal distribution was the best fittedone, which is mainly for the copolarized channels over PF andadvanced regeneration stages. As log normal does not followthe multiplicative model, this fact might be explained by theinteraction of the long wavelength with forests, which does notagree with the usual assumptions of the multiplicative model[53], considering that the resolution cell and P-band wavelengthhave the same order of magnitude. In addition, a mixture ofdifferent scattering mechanisms can be present on the radarreturn, particularly over the PF. It should be noticed that theassumption of log-normal to the amplitude data is equivalent toassuming a Gaussian distribution for the backscatter expressedin decibels, as assumed by Hoekman and Quiñones [23]. Forthe full polarimetric classification, the multivariate G0 waschosen for all classes, except for CP that was better fitted by themultivariate K distribution. This flexibility of the G distributionis mentioned by Mejail et al. [54], [55], who observed that theGI distribution can appropriately describe homogeneous andheterogeneous areas, besides extremely heterogeneous clutter.

The performance of the ML/ICM classifications is quantifiedin Table II by the κ̂ values and its variances for the three sets ofclasses. The results of the best ICM classification for each setof classes are shown in Fig. 4. The statistical tests evaluatingthe equality between each pair of κ values per set of classesare shown in Table III. The low κ̂ values for the eight classes’case indicate a large confusion among the classes. The testsof equality of κ demonstrated that most classifications can beconsidered different from each other at a significance level of5%. However, there was no statistical evidence of differencesbetween the κ values of the pairs of classifications, whichare amplitudes HH and HV, amplitude VV and multivariateHH–HV–VV, bivariate HH–HV and full polarimetric, and thetwo multivariate images.

Observing the second column of Table II, it can be noticedthat the BMI classification showed the best κ̂ value; however,the amplitude VV and the bivariate HV–VV were also consid-ered among the best channel combinations to separate the set ofeight classes. The higher κ̂ value for the BMI classification can


TABLE IIKAPPA COEFFICIENT OF AGREEMENT VALUES FOR ALL THE CLASSIFICATIONS PERFORMED.

BOLD VALUES INDICATE THE HIGHER VALUES FOR EACH SET OF CLASSES

be explained by its good classification for VO, OR, IR, and BSclasses, which is shown in Fig. 4(a) and in the confusion matrixin Table IV(a). All other classes, except CP, had a lower orequivalent percentage of correct pixels classified in comparisonwith the amplitude VV classification, whose confusion matrixis given in Table IV(b). In general, the confusion among theregeneration classes (VO, OR, IR, and NR) and between BSand CP is smaller in the BMI than in the VV classification.A possible explanation for the better quality of the BMI clas-sification is the fact that it contains information about theP-band HH component, which is more adequate for estimatingsoil moisture than the VV component [56].

The BMI classification was also better than the bivariateHV–VV (results not shown here) for a considerable group ofclasses in terms of the corrected classified pixels, particularlyfor old stages of regeneration. It may indicate that the HVcomponent does not significantly contribute to the classifica-tion of these regeneration stages when it is added to the VVcomponent. Thus, considering that BMI contains informationabout HH component, one may say that the HH showed betterresults than HV for differentiating old stages of regenerationfrom other classes. These statements are connected to thecanopy backscatter theory that HV backscatter predominantlycomes from tree crown scattering [57] and to the fact thatHH backscatter was mainly due to trunk–ground interactions[58]. Hence, the HV response is dominant among low biomassareas as initial and intermediate secondary forest, which issimilar to previous results in tropical forest areas [59] and inmangrove forests, where the canopy structure is sparse to dense,as observed by Proisy et al. [56].

It can be seen from Table IV(a) and (b) that there is con-siderable confusion among PF, VO, and OR and also amongOR, IR, and NR. The same occurs between classes CP andBS. The confusion matrices for other classifications (not pre-

sented here) showed similar patterns of confusion among theseclasses. However, the simple comparison among the number ofmisclassified pixels, using the confusion matrices of the bestclassifications, seems to be insufficient for class merging. Thus,a detailed graphical analysis was adopted by using copolarizedand depolarized factors, average values of backscatter for eachpolarization, average values of entropy, α angle and anisotropy,and, finally, H/α plans of the different training samples.

The first important results appeared in the analysis of av-erage values of the backscatter and the entropy, α angle, andanisotropy images. The first graph of Fig. 5(a) shows that,particularly for entropy values, there is no difference of thescattering mechanisms among the classes IR, NR, CP, andBS. However, the average values of α angle indicate a moreappropriate class merging when grouping IR+NR and thenBS+CP. These indications are reinforced by the average valuesof entropy, α angle, and anisotropy simulating the class mergingfrom eight to five and four classes, as shown in the two graphsof Fig. 5(a).

A similar analysis for class merging can be drawn from theH/α plan. The H/α plans have been used to differentiate thebackscatter responses of vegetation using SAR polarimetricdata [60]. This tool is particularly useful for identifying thetype of backscatter mechanism according to the relative zonewhere samples are concentrated in the plan [48]. Thus, inFig. 5(b)–(d), one can see that FO, VO, and OR tend toconcentrate in zones 4 and 5; IR and NR in zone 4; and, finally,CP and BS cluster into zones 4, 5, and 6. Based on these results,a class merging (into five and four classes) was applied, andthe classifications were performed afterward. Table I shows themerged classes.

Similar to the case of the eight classes, for five classes, it wasobserved that most of the classes were better fitted by eitherlog-normal or G distributions for the individual polarimetric


Fig. 4. Classified images. (a) Eight classes’ case using BMI data. (b) Five classes’ case using amplitude HV data. (c) Four classes’ case using bivariate intensityHV–VV data. Refer to the colored bars for the corresponding classes.

channel classifications and by the multivariate G for the fullpolarimetric case. The estimated κ̂ values and the correspond-ing variances for the classifications of five classes are givenon the fourth and fifth columns of Table II, and a comparisonamong the most significant classification is shown in Table III.When only individual channels are used, the higher κ̂ valueis reached with the amplitude HV classification, which wasalso the highest κ̂ value among all classifications for the fiveclasses’ case. The classification of the amplitude HV image andthe corresponding confusion matrix are shown in Fig. 4(b) andTable V(a), respectively. At 5% of the significance level, theκ̂ value for the bivariate HH–HV and amplitude HV can beconsidered equal, as illustrated in Tables II and III, showingthat the classification results were not improved by the additionof the HH component. The confusion matrix for the HH–HVclassification is given in Table V(b). In comparison with theamplitude HV classification, the bivariate HH–HV presented adecrease in the number of OR correct classified pixels. It leadsto the conclusion that the HH component, in addition to HV,

does not present a classification improvement to this class butthat it does to PF+VO and IR+NR classes. It indicates thatHV is more sensitive to biomass changes in tropical forest, asobserved by Hoekman and Quiñones [18], [59]. Thus, the HVcomponent is more efficient in differentiating among severalstages of forest and recent stages of regeneration. Similarresults from the P-band can be found in [61], which is aninvestigation with a data set acquired in Guyana and Colombiatropical rain forests.

The adjustment of the distributions for the classificationswith four classes follows the same pattern observed for thecases of eight and five classes. The estimated κ̂ values and itscorresponding variances for the classifications into four classesare given on the sixth and seventh columns of Table II. Thedifference here is the higher κ̂ value that was reached by twoclassifications considered to be statistically similar at the 5%significance level—the bivariate HV–VV and the multivariateHH–HV–VV. This result shows that the addition of the HHcomponent does not improve the classification results, which


TABLE IIIRESULTS OF THE COMPARISON AMONG THE CLASSIFICATIONS CONSIDERED THE BEST PER CATEGORY FOR EACH CLASS SET

TABLE IVCONFUSION MATRIX (PERCENTAGE OF PIXELS) WITH EIGHT CLASSES USING (a) BMI AND (b) VV CLASSIFICATIONS

is as also noted by Hoekman and Quiñones [18], where theP-band polarimetric data classifications in a tropical forestsite were not improved by the addition of a third component.Thus, the bivariate HV–VV should be considered to be thebest classification for this class merging. The correspondingclassified image and confusion matrix are shown in Fig. 4(c)and Table VI, respectively.

Even considering that an individual polarimetric channel wasnot pointed as the one having the best κ̂ value for the fourclasses’ case, these results confirm that components HV and/orVV tend to be more suitable to distinguish different land useclasses in the presence of different stages of secondary forest.

In Table III, one perceives that the statistical equality amongthe classifications increases according to the class generaliza-tion. Although the full polarimetric classification includes allP-band components, it does not necessarily mean that a betterresult can be reached by using all components. This is thephenomenon known as the curse of dimensionality [62], [63],

which can be explained by the fact that, usually, the training andtest data sizes are kept constant while one experiments with avariable number of information source channels. After a certainpoint, which is generally close to what is known as the intrinsicdimensionality of the data [64], the estimation variance (neededfor classification algorithms) of the parameters increases to acertain level, and the classification error also increases, leadingto an increase in data misclassification.

Regarding the polarimetric parameters α angle, anisotropy,and entropy, its individual classifications showed the worstresults for all the sets of classes. In addition, when theseparameters were added to the multivariate classifications,they did not significantly improve these classifications.This can be observed by the results for eight and fiveclasses, where the multivariate HH–HV–VV and multivari-ate HH–HV–VV–Alpha–anisotropy–entropy were statisticallysimilar at a significance level of 5%. In the four classes’ case,the inclusion of these parameters even reduced the κ̂ values


Fig. 5. Graphics of entropy (H), α angle, and anisotropy showing the confusion among the classes that justify the class merging. (a) Mean values of land usetraining samples taken from entropy, α angle, and anisotropy images. (b) H/α planes for PF, VO, and OR classes, separately and their combination. (c) H/αplanes for IR and NR classes, separately and their combination. (d) H/α planes for BS and CP, separately and their combination.

between the multivariate classifications, which is probably be-cause of the curse of dimensionality.

In order to exemplify the improvement in the classificationaccuracy using adequate distributions for SAR data, Table VIIshows the κ̂ values for the eight, five, and four classes’ cases

for the bivariate ML classifications. These classifications wereperformed by using either the Gaussian distributions for allclasses, which are usually found in geographic informationsystems, or the bivariate distribution derived from the Wishartdistribution (6). One-sided tests for the differences between


TABLE VCONFUSION MATRIX (PERCENTAGE OF PIXELS) USING (a) AMPLITUDE HV AND (b) BIVARIATE HH–HV WITH FIVE CLASSES

TABLE VICONFUSION MATRIX (PERCENTAGE OF PIXELS) USING THE

BIVARIATE HV–VV WITH FOUR CLASSES

pairs of κ̂ values (Gaussian versus proper distributions) showedthat they are all statistically different at a 5% significance level.The average improvement on the overall accuracy1 was 14.5%higher than the overall accuracy obtained only with a Gaussianhypothesis. Comparing the bivariate ML and ICM (given inTable II), it is possible to see an average improvement of about32.3% in the κ̂ values.

V. SUMMARY AND CONCLUSION

A set of experimental P-band SAR images were taken in theTapajós National Forest in September 2000, using an airborneradar system developed by AeroSensing Radarsysteme GmbH,with a pixel spacing of 1.5 × 0.67 m in slant range and in one-look polarimetric channels (HH, HV, VH, and VV). At the sametime, during a field survey, in situ data were collected, whichwere used as input in supervised classification procedures. Thesystem permits the classifications of individual or a combi-nation of amplitude/intensity images, full polarimetric, pairof intensities, difference of phase images, ratio of intensities,and a pair of intensity-difference phase images. It also per-formed classifications using entropy, α angle, and anisotropyparameters and also an analysis of backscatter coefficients,copolarization and depolarization ratios, and the magnitude ofthe correlation coefficients.

1Overall accuracy is defined as the percentage of pixels correctly classified.It is computed from the confusion matrix (given in pixels) by the ratio betweenthe sum of the main diagonal elements and the total number of pixels.

The results with eight classes were poor, indicating that theP-band is not suitable for the differentiation of too many clas-ses. Considering the results presented in [18] and [23], whereseveral land use/cover classes could be mainly differentiated bythe multifrequency combinations of L- and P-bands, we con-clude that the use of P-band only might have limited the classi-fication method used here for the eight classes’ case. However,several considerations must be made, which are as follows.

1) When four classes were considered (see Table II), thebest classification was obtained by bivariate HV–VV,and no improvement in the classification accuracy wasobserved when HH channel was used in conjunction withHV and VV. Similar conclusions have been addressedregarding the poor contribution of a third channel in theclassification accuracy [18].

2) The amplitude HV and BMI were considered the bestclassifications for the five and eight classes’ cases,respectively.

3) It seems that HV is more efficient in differentiating forestand advanced stages of secondary forest from other landcover classes, considering that HV is more sensitiveto changes in biomass [18], whereas VV improves theclassification for BS areas.

4) The polarimetric parameters α angle, anisotropy, andentropy did not improve the discrimination of land useclasses, i.e., these parameters are ineffective in distin-guishing land use classes with the P-band data.

5) On the other hand, the H/α plans properly justified theclass merging considered. They showed that the multiplevolumetric scattering types are dominant for most classes,and the surface scattering is present in some samples ofthe BS and CP classes. However, there is no dominantbackscatter mechanism.

6) For the P-band full polarimetric data and the set ofclasses used in this paper, the curse of dimensionalityis reached relatively soon because, as seen in Tables IIand III, the best classification result is achieved with onlytwo channels, which is much less than the number offive potentially independent parameters of the complexcovariance matrix.

7) The use of adequate distributions for SAR data, which isgiven by (3)–(6), for statistical classifications improves


TABLE VIIKAPPA VALUES FOR THE ML CLASSIFICATIONS OF PAIR INTENSITIES (BIVARIATES) USING

THE GAUSSIAN AND THE ADEQUATE SAR (6) DISTRIBUTIONS

the classification results. Bivariate cases are, in general,responsible for the best classification results using theadequate distribution, and these also proved to be betterthan the Gaussian hypothesis. Therefore, it is suggestedthat such a procedure should be generally used for thebivariate statistical SAR classification.

8) Besides the wide acceptance of the multiplicative model,we showed the convenience of using distributions that arenot directed to justify the multiplicative model, such asthe log-normal distribution, to fit those cases where thescattering mechanism is dominated by a few scatterers.

9) The use of contextual information, in this case exploredby the ICM algorithm, improved the classification accu-racy. Hoekman and Quiñones [23], although using multi-frequency complex coherence images and not consideringdifferent distributions to adjust the classes, also observeda significant improvement at the overall accuracy whenconsidering the ICM method, for a Colombian tropicalforest site.

10) The a priori established accuracy of a Kappa value of75% was obtained only with four classes and two andthree channels. Considering that the three-channel caseis not statistically different from the two-channel case,the use of only two channels is preferable for the sakeof simplicity. Thus, as a consequence, it is possible toconclude that there is no need to use more than twochannels to get most of the P-band data for tropical forestland cover discrimination purposes.

The findings of this paper indicate that a possible P-bandspaceborne mission would be valuable to effectively discrim-inate, at least, three levels of biomass, with only two channels.A spaceborne mission may provide all channels but with areduced resolution, which is not enough to detect, for instance,clearings of about 10–15 m in diameter caused by selectivelogging of commercial tree species. Therefore, when planningspaceborne P-band missions, it would be preferable to givepriority to the spatial resolution instead of the full polari-metric capability for tropical forest deforestation/degradationmonitoring.

This paper will continue to consider feature extraction, suchas texture and other decompositions. Lee et al. [65] mentionthat forest classification is greatly improved when using L-bandInSAR products. To test if P-band InSAR products will alsoincrease the accuracy, a repeat track P-band InSAR acquisitioncampaign is planned for the near future.

REFERENCES

[1] J. R. Santos, C. C. Freitas, L. S. Araújo, L. D. Dutra, J. C. Mura,F. F. Gama, L. S. Soler, and S. J. S. Sant’Anna, “Airborne P-bandSAR applied to the above ground biomass studies in the Brazilian trop-ical rainforest,” Remote Sens. Environ., vol. 87, no. 4, pp. 482–493,Nov. 2003.

[2] INPE, Monitoring of the Brazilian Amazonian PRODES 2005–2006,São José dos Campos, Brazil. accessed in September 2007. [Online].Available: http://www.obt.inpe.br/prodes/

[3] T. Neeff, R. M. Lucas, J. R. Santos, L. V. Dutra, E. S. Brondizio, andC. C. Freitas, “Area and age of secondary forests in Brazilian Amazonia1978–2002: An empirical estimate,” Ecosystems, vol. 9, no. 4, pp. 609–623, Jun. 2006.

[4] F. M. Fearnside, “Amazonian deforestation and global warming: Carbonstocks in vegetation replacing Brazil’s Amazon forest,” For. Ecol. Manag.,vol. 80, no. 1, pp. 21–34, Jan. 1996.

[5] R. M. Lucas, M. Honzák, P. J. Curran, G. M. Foody, R. Milnes,T. Brown, and S. Amaral, “Mapping the regional extent of tropical forestregeneration stages in the Brazilian Legal Amazon using NOAA AVHRRdata,” Int. J. Remote Sens., vol. 21, no. 15, pp. 2855–2881, Oct. 2000.

[6] E. F. Moran, E. S. Brondizio, J. M. Tucker, M. C. da Silva-Forsberg,S. McCracken, and I. Falesi, “Effects of soil fertility and land-use on forestsuccession in Amazonia,” For. Ecol. Manag., vol. 139, no. 1–3, pp. 93–108, Dec. 2000.

[7] J. Shukla, C. Nobre, and P. Sellers, “Amazon deforestation and climatechange,” Science, vol. 247, no. 4948, pp. 1322–1325, Mar. 1990.

[8] E. O. Wilson, Biodiversity, 1st ed. Washington, DC: Nat. AcademicPress, 1988.

[9] Ministério da Ciência e Tecnologia (MCT-PNUD), Primeiro InventárioBrasileiro de Emissões Antrópicas de Gases de Efeito Estufa. Emissões eremoções de dióxido de carbono por convesão de florestas e abandono deterras cultivadas. Relatórios de Referência, 2006.

[10] J. R. Baker, P. L. Mitchell, R. A. Cordey, G. B. Groom, J. J. Settle,and M. R. Stileman, “Relationships between physical characteristics andpolarimetric radar backscatter for Corsican pine stands in Thetford Forest,U.K.,” Int. J. Remote Sens., vol. 15, no. 14, pp. 2827–2849, Sep. 1994.

[11] M. L. Imhoff, “Radar backscatter and biomass saturation: Ramificationsfor global biomass inventory,” IEEE Trans. Geosci. Remote Sens., vol. 33,no. 2, pp. 511–518, Mar. 1995.

[12] G. M. Foody, R. M. Green, R. M. Lucas, P. J. Curran, M. Honzak, andI. D. Amaral, “Observations on the relationship between SIR-C radarbackscatter and the biomass of regenerating tropical forests,” Int. J. Re-mote Sens., vol. 18, no. 3, pp. 687–694, Feb. 1997.

[13] A. Luckman, J. Baker, M. Honzak, and R. Lucas, “Tropical forest biomassdensity estimation using JERS-1 SAR: Seasonal variation, confidencelimits, and application to image mosaics,” Remote Sens. Environ., vol. 63,no. 2, pp. 126–139, Feb. 1998.

[14] C. C. Freitas, S. J. S. Sant’Anna, L. S. Soler, J. R. Santos, L. V. Dutra,L. S. Araújo, J. C. Mura, and P. Hernandez Filho, “The use of airborneP-band radar data for land use and land cover mapping in Brazilian Ama-zonia,” in Proc. IGARSS, Sydney, Australia, Jul. 9–13, 2001, pp. 1889–1891. CD-ROM.

[15] L. V. Dutra, P. Hernandez Filho, M. E. Mazzocato, R. C. M. Souza, andC. Oliver, “Land cover classification based on multi-date JERS-1 im-agery as a basis for deforestation detection,” in Proc. IGARSS, Hamburg,Germany, Jun. 26–Jul. 2 1999, pp. 2759–2761. CD-ROM.

[16] National Space Development Agency of Japan (NASDA), JapaneseEarth Resource Satellite (JERS-1), Tokyo, Japan. accessed in March 2008.[Online]. Available: http://www.eorc.jaxa.jp/JERS-1

[17] PALSAR Project, Tokyo, Japan: Japan Aerospace Exploration Agency(JAXA).


[18] D. H. Hoekman and M. J. Quiñones, “Land cover type and biomassclassification using AirSAR data for evaluation of monitoring scenariosin the Colombian Amazon,” IEEE Trans. Geosci. Remote Sens., vol. 38,no. 2, pp. 685–696, Mar. 2000.

[19] "Biomass Monitoring Mission for Carbon Assessment (BIOMASS),"ESA Earth Explorer Opportunity Missions, Toulouse, France: CESBIO.accessed in March 2008. [Online]. Available: http://www.cesbio.ups-tlse.fr/us/indexbiomass.html

[20] T. Le Toan, A. Beaudoin, and D. Guyon, “Relating forest biomass to SARdata,” IEEE Trans. Geosci. Remote Sens., vol. 30, no. 2, pp. 403–411,Mar. 1992.

[21] A. Beaudoin, T. Le Toan, S. Goze, E. Nezry, A. Lopes, E. Mougin,C. C. Hsu, H. C. Han, J. A. Kong, and R. T. Shin, “Retrieval of forestbiomass from SAR data,” Int. J. Remote Sens., vol. 15, no. 14, pp. 2777–2796, 1994.

[22] T. Neeff, L. V. Dutra, J. R. Santos, C. C. Freitas, and L. S. Araújo,“Tropical forest biomass measurement by interferometric height mod-elling and P-band radar backscatter,” For. Sci., vol. 51, no. 6, pp. 585–594,Dec. 2005.

[23] D. H. Hoekman and M. J. Quiñones, “Biophysical forest type charac-terization in the Colombian Amazon by airborne polarimetric SAR,”IEEE Trans. Geosci. Remote Sens., vol. 40, no. 6, pp. 1288–1300,Jun. 2002.

[24] A. C. Frery, H. J. Müller, C. C. F. Yanasse, and S. J. S. Sant’Anna, “Amodel for extremely heterogeneous clutter,” IEEE Trans. Geosci. RemoteSens., vol. 35, no. 3, pp. 648–659, May 1997.

[25] A. C. Frery, C. C. F. Yanasse, P. R. Vieira, S. J. S. Sant’Anna, andC. D. Rennó, “A user-friendly system for synthetic aperture radar imageclassification based on grayscale distributional properties and context,” inProc. IEEE Simpósio Brasileiro de Computação Gráfica e Processamentode Imagens, Campos de Jordão, Brazil, Oct. 14–17, 1997, pp. 211–218.

[26] A. H. Correia, “Desenvolvimento e avaliação de classificadores estatísti-cos pontuais e contextuais para imagens SAR polarimétricas,” M.S. thesis,Inst. Nac. Pesquisas Espaciais, São José dos Campos, Brazil, 1998.

[27] J. Landis and G. G. Koch, “The measurements of observer agreement forcategorical data,” Biometrics, vol. 33, no. 3, pp. 159–174, Mar. 1977.

[28] “Projeto RADAMBRASIL, Folha SA,” 21 Santarém, Geologia, Geo-morfologia, Solos, Vegetação e Uso potencial da terra. Levantamento deRecursos Naturais, vol. 10, 1976, Rio de Janeiro, Brazil.

[29] J. Moreira, “Design of an airborne interferometric SAR for high preci-sion DEM generation,” in Proc. XVIII ISPRS Congr., Vienna, Austria,Jul. 9–19, 1996, vol. XXI, pp. 256–260. Commission II.

[30] I. C. G. Vieira, A. S. Almeida, E. A. Davidson, T. A. Stone,C. J. R. Carvalho, and J. B. Guerrero, “Classifying successional forestsusing Landsat spectral properties and ecological characteristics in east-ern Amazônia,” Remote Sens. Environ., vol. 87, no. 4, pp. 470–481,Nov. 2003.

[31] J. S. Lee, M. R. Grunes, and E. Pottier, “Quantitative comparison of classi-fication capability: Fully polarimetric versus dual and single-polarizationSAR,” IEEE Trans. Geosci. Remote Sens., vol. 39, no. 11, pp. 2343–2351,Nov. 2001.

[32] W. M. Boerner et al., “Polarimetry in radar remote sensing: Basic and ap-plied concepts,” in Principles and Applications of Imaging Radar, Manualof Remote Sensing, 3rd ed. New York: Wiley, 1998, ch. 5.

[33] J. S. Lee, D. L. DuShuler, and M. R. Grunes, “Statistical analysis andsegmentation of multi-look SAR imagery using partial polarimetric data,”in Proc. IGARSS, Firenze, Italy, Jul. 10–14, 1995, vol. 2, pp. 1422–1424.

[34] N. R. Goodman, “Statistical analysis based on a certain multivariate com-plex Gaussian distributions (An introduction),” Ann. Math. Stat., vol. 34,no. 1, pp. 152–177, Mar. 1963.

[35] M. S. Srivastava, “On the complex Wishart distribution,” Ann. Math. Stat.,vol. 36, no. 1, pp. 313–315, Feb. 1963.

[36] J. S. Lee, K. W. Hoppel, S. A. Mango, and A. R. Miller, “Intensityand phase statistics of multilook polarimetric and interferometric SARimagery,” IEEE Trans. Geosci. Remote Sens., vol. 32, no. 5, pp. 1017–1028, Sep. 1994.

[37] C. C. Freitas, A. C. Frery, and A. H. Correia, “The polarimetric G distri-butions for SAR data analysis,” Environmetrics, vol. 16, no. 1, pp. 13–31,Feb. 2005.

[38] J. S. Lee, D. L. Schuler, R. H. Lang, and K. J. Ranson, “K-distributionfor multi-look processed polarimetric SAR imagery,” in Proc. IGARSS,Pasadena, CA, Aug. 8–12, 1994, vol. 4, pp. 2179–2181.

[39] J. S. Lee, L. Du, D. L. Shuler, and M. R. Grunes, “Statistical analysis andsegmentation of multilook SAR imagery using partial polarimetric data,”in Proc. IGARSS, Firenze, Italy, Jul. 10–14, 1995, vol. 3, pp. 1422–1424.

[40] S. Geman and D. Geman, “Stochastic relaxation, Gibbs distributions, andthe Bayesian restoration of images,” IEEE Trans. Pattern Anal. Mach.Intell., vol. PAMI-6, no. 6, pp. 721–741, Nov. 1984.

[41] J. Besag, “Towards Bayesian image analysis,” J. Appl. Stat., vol. 16, no. 3,pp. 395–407, 1989.

[42] A. C. Frery, C. C. Freitas, and A. H. Correia, “Classifying multifrequencyfully polarimetric imagery with multiple sources of statistical evidenceand contextual information,” IEEE Trans. Geosci. Remote Sens., vol. 45,no. 10, pp. 3098–3109, Oct. 2007.

[43] P. R. Vieira, “Desenvolvimento de classificadores de máxima verossim-ilhança e ICM para imagens SAR,” M.S. thesis, Inst. Nac. PesquisasEspaciais, São José dos Campos, Brazil, 1996.

[44] A. H. Correia, C. C. Freitas, A. C. Frery, and S. J. S. Sant’Anna, “Auser friendly statistical system for polarimetric SAR image classification,”Revista de Teledetección, Sociedade Espanhola de Teledetección, vol. 10,pp. 79–93, 1998.

[45] S. Quegan, “A unified algorithm for phase and cross-talk calibration of po-larimetric data—Theory and observations,” IEEE Trans. Geosci. RemoteSens., vol. 32, no. 1, pp. 89–99, Jan. 1994.

[46] K. O. Pope, J. M. Rey-Benayas, and J. F. Paris, “Radar remote sensing offorest and wetland ecosystems in the Central American Tropics,” RemoteSens. Environ., vol. 48, no. 2, pp. 205–219, May 1994.

[47] S. R. Cloude and E. Pottier, “A review of target decomposition theoremsin radar polarimetry,” IEEE Trans. Geosci. Remote Sens., vol. 34, no. 2,pp. 498–518, Mar. 1996.

[48] S. R. Cloude and E. Pottier, “An entropy based classification schemefor land applications of polarimetric SAR,” IEEE Trans. Geosci. RemoteSens., vol. 35, no. 1, pp. 68–78, Jan. 1997.

[49] E. Pottier, “The polarimetric decomposition approach applied to pol-sar data processing,” in Proc. PIERS Workshop Advances RadarMethods, Baveno, Italy, Jul. 20–22, 1998, pp. 120–122. Rep. EUR18662 EN.

[50] J. S. Lee, M. R. Grunes, T. L. Ainsworth, L. J. Du, D. J Schuler, andS. R. Cloude, “Unsupervised classification using polarimetric decompo-sition and the complex Wishart classifier,” IEEE Trans. Geosci. RemoteSens., vol. 37, no. 5, pp. 2249–2258, Sep. 1999.

[51] Y. S. Bishop, S. E. Fienberg, and P. W. Holland, Discrete MultivariateAnalysis—Theory and Practice, 1st ed. Cambridge, MA: MIT Press,1975.

[52] R. G. Congalton and K. Green, Assessing the Accuracy of RemotelySensed Data: Principles and Practices, 1st ed. New York: LewisPublishers, 1999.

[53] C. Oliver and S. Quegan, Understanding Synthetic Aperture RadarImages, 1st ed. Boston, MA: Artech House, 1998.

[54] M. E. Mejail, J. Jacobo-Berlles, A. C. Frery, and O. H. Bustos, “Paramet-ric roughness estimation in amplitude SAR images under the multiplica-tive model,” Revista de Teledección, vol. 13, pp. 37–49, 2000.

[55] M. E. Mejail, A. C. Frery, J. Jacobo-Berlles, and O. H. Bustos, “Approxi-mation of distributions for SAR images: Proposal, evaluation and practicalconsequences,” Latin Amer. Appl. Res., vol. 31, pp. 83–92, 2001.

[56] C. Proisy, E. Mougin, F. Fromard, and M. A. Karam, “Interpretation of po-larimetric radar signatures of mangrove forests,” Remote Sens. Environ.,vol. 71, no. 1, pp. 56–66, Jan. 2000.

[57] Y. Wang, L. L. Hess, S. Filoso, and J. M. Melack, “Understanding theradar backscattering from flooded and nonflooded Amazonian forests:Results from canopy backscatter modeling,” Remote Sens. Environ.,vol. 54, no. 3, pp. 324–332, Dec. 1995.

[58] Y. Wang, F. W. Davis, and J. M. Melack, “Simulated and observedbackscatter at P-, L-, and C-bands from ponderosa pine stands,” IEEETrans. Geosci. Remote Sens., vol. 31, no. 4, pp. 871–879, Jul. 1993.

[59] M. J. Quiñones and D. H. Hoekman, “Exploration of factors limitingbiomass estimation by polarimetric radar in tropical forests,” IEEE Trans.Geosci. Remote Sens., vol. 42, no. 1, pp. 86–104, Jan. 2004.

[60] L. Ferro-Famil and E. Pottier, “Unsupervised classification ofmultifrequency and fully polarimetric SAR images based on theH/A/Alpha–Wishart classifier,” IEEE Trans. Geosci. Remote Sens.,vol. 39, no. 11, pp. 2332–2342, Nov. 2001.

[61] J. J. van der Sanden and D. H. Hoekman, “Potential of airborne radar tosupport the assessment of land cover in a tropical rain forest environment,”Remote Sens. Environ., vol. 68, no. 1, pp. 26–40, Apr. 1999.

[62] C. M. Bishop, Neural Networks for Pattern Recognition, 1st ed. Oxford,U.K.: Clarendon, 1995.

[63] L. V. Dutra and R. Huber, “Feature extraction and selection for land useclassification for ERS-1/2 InSar data,” Int. J. Remote Sens., vol. 20, no. 5,pp. 993–1016, Mar. 1999.

[64] K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd ed.New York: Academic, 1990.

[65] J. S. Lee, K. P. Papathanassiou, I. Hajnsek, T. Mette, M. R. Grunes,T. Ainsworth, and L. Ferro-Famil, “Applying polarimetric SAR interfero-metric data for forest classification,” in Proc. IGARSS, Jul. 25–29, 2005,vol. 7, pp. 4848–4851.


Corina da Costa Freitas received the B.Sc. degreein mathematics from the Pontifical Catholic Univer-sity of São Paulo, São Paulo, Brazil, the M.Sc. degreein statistics from the Massachusetts Institute of Tech-nology, Cambridge, in 1980, and the Ph.D. degree instatistics from Sheffield University, Sheffield, U.K.,in 1992.

She is currently a Senior Researcher with the Im-age Processing Division, National Institute for SpaceResearch, São José dos Campos, Brazil. Her researchinterests include radar image processing and radar

polarimetry.

Luciana de Souza Soler received the B.Sc. degree inphysics from the Federal University of Mato Grossodo Sul, Campo Grande, Brazil, in 1997 and theM.Sc. degree in remote sensing from the NationalInstitute for Space Research (INPE), São José dosCampos, Brazil, in 2000. She is currently work-ing toward the Ph.D. degree in production ecologyand resource conservation at Wageningen University,Wageningen, The Netherlands.

She was a Technologist with the Federal Univer-sity of Rio de Janeiro, Rio de Janeiro, Brazil, as part

of a World Bank project to monitor oil spills in the Brazilian coast using SARimages. She is currently with Wageningen University. Her research interestsinclude radar image processing and land-use/cover monitoring/modeling in theBrazilian Amazon.

Sidnei João Siqueira Sant’Anna received the de-gree in electrical and electronics engineering fromthe Federal University of Rio de Janeiro, Rio deJaneiro, Brazil, in 1993 and the M.Sc. degree inremote sensing from the National Institute for SpaceResearch (INPE), São José dos Campos, Brazil, in1995. He is currently working toward the Ph.D.degree in electrical engineering at the TechnologicalInstitute of Aeronautics, São José dos Campos.

He is a Technologist with the Image ProcessingDivision, INPE, and his interests are radar image

analysis, radar polarimetry, and electromagnetic scattering modeling.

Luciano Vieira Dutra received the B.Sc. degree inelectronics engineering from the Technological In-stitute of Aeronautics, São José dos Campos, Brazil,in 1976 and the M.Sc. and Ph.D. degrees in com-puter science from the National Institute for SpaceResearch (INPE), São José dos Campos, in 1981 and1989, respectively.

In 1991, he was a Visiting Scholar with SheffieldUniversity, Sheffield, U.K. In 1995, 1997, and 1998,he was a Visiting Scientist, on the subject of radarinformation extraction, with the German Aerospace

Center, Oberpfaffenhofen. Since 1977, he has been with INPE, where he iscurrently a Senior Researcher with the Image Processing Division. He is alsoa Principal Investigator for the ALOS Research Program, as an INPE repre-sentative. His research interests include SAR image processing, interferometry,pattern recognition, and remote sensing applications.

João Roberto dos Santos received the B.Sc. degreein forestry engineering from the Federal Rural Uni-versity of Rio de Janeiro, Rio de Janeiro, Brazil,in 1974, the M.Sc. degree in remote sensing fromthe National Institute for Space Research (INPE),São José dos Campos, Brazil, in 1979, and the Ph.D.degree in forest science from the Federal Universityof Paraná, Curitiba, Brazil, in 1988.

He is a Senior Researcher with the Remote Sens-ing Division, INPE. His research interests are opticalimages and SAR applications for the inventory and

monitoring of forest typologies.

José Claudio Mura received the degree in elec-tric engineering from the University of São Paulo,São Carlos, Brazil, in 1978, the M.Sc. degree in elec-tronics and telecommunication from the Technologi-cal Institute of Aeronautics, São José dos Campos,Brazil, in 1985, and the Ph.D. degree in appliedcomputation from the National Institute for SpaceResearch (INPE), São José dos Campos, in 2000.

He is currently a Senior Technologist with theImage Processing Division, INPE. His research inter-ests include radar image processing, radar polarime-

try, and interferometry.

Antônio Henrique Correia received the B.Sc. de-gree in cartographic engineering from the MilitaryInstitute of Engineering, Rio de Janeiro, Brazil, andthe M.Sc. degree in remote sensing from the NationalInstitute of Space Research, São José dos Campos,Brazil, in 1998, where he is currently working towardthe Ph.D. degree in remote sensing.

His research interests include radar image process-ing and radar polarimetry.

land use and land cover mapping in the brazilian amazon using polarimetric airborne p-band sar data

Documents