multispectral satellite image and ancillary data integration ......multispectral satellite image and...

Multispectral Satellite Image and Ancillary Data Integration for

Geological Classification Evaristo Ricchetti

Abstract Digital classification of Landsat imagery for geological pur- poses often gives poor results. To improve classification accuracy spectral data have been combined with ancillary data. These data have been used in pre-classification processing to enhance image quality, and as additional attribute information during the classification process.

Several geological classifications were conducted using various levels of integrated spectral and topographic data. A slope map was used to add information about the geomorphologic nature of the different geological units. Logical channel and stratification methods were applied and compared to a spectral classification.

The classification results demonstrate that a significant increase in overall accuracy can be achieved by combining topographic data with spectral data. The use of stratification, in addition to a logical channel, did not give an evident improvement in overall accuracy.

The use of ancillary data in image classification must rely on in-depth knowledge of the target to select the attribute that best characterizes it.

lntroductlon Automated image classification techniques, when applied to geological/lithological studies in high relief temperate regions, often produce poor results. In these areas, multispectral data may not provide sufficient information for reliable identifica- tion of geological formations due to the widespread vegetation cover &d thi strong dependence of reflected ene rgyn topography.

Classification results of spectral data can be improved by taking into account other object attributes. The simplest way is to incorporate information from ancillary data sources such as topographic maps. There are many examples of integrating ancillary data, in particular topographic data, with multispectral data to improve interpretation potential (Hutchinson, 1982; Franklin, 1989; Franklin and Wilson, 1991; Duguay et al., 1989).

The purpose of this study is to evaluate the effectiveness of using topographic data to improve the interpretability of image information, and the automatic classification of spectral data for geological studies. Geological formations often show a strong correlation with geomorphologic features and, therefore, topographic attribute data such as can be provided by aDTM are particularly useful when integrated with spectral data.

A Landsat TM image, acquired on 26 July 1991, and avector topographic map with contours were used in this study.

Department of Geology and Geophysics, University of Bari, Via Orabona, 4, 70125 Bari, Italy ([email protected]).

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Study Area The study area, in southwestern Calabria (southern Italy), is a high relief area with its peak, of about 1950 m a.s.l., far less than 20 km from the coastline. The geomorphologic setting is characterized by a radial fluvial pattern, with deep and narrow centrifugal valleys, and several terrace flights from more than 1400 m a.s.1. down to the coastline.

This study focuses on the deposits of Pliocene-Quaternary age that widely outcrop along the coastal zones and the terrace flights. These deposits can be divided into four main geological units: calcarenite and sand (Upper Pliocene-Lower Pleisto- cene), fan conglomerates and sand (Upper Pleistocene), terrace deposits (Upper Pleistocene-Holocene), and alluvial deposits (Holocene).

Calcarenite and sand were deposited along marine abra- sion surfaces; their setting is horizontal and the maximum outcropping thickness is about 200 m. These deposits generally outcrop at the top of valley slopes, often covered by terrace deposits, and are characterized by steep cliffs. Fan conglomerates and sand locally outcrop in the coastal area with a typical convex fan shape. These deposits are clino-stratified and dip at an angle of about 35". Terrace deposits widely outcrop along flat terrace flights and are characterized by a decreasing texture from gravel and sand in near coastal outcrops to silt in the most elevated outcrops. These deposits have a horizontal setting and are generally thin, with a maximum thickness of about 30 m. Alluvial deposits extensively cover the coastal plain in the lower portion of the river valleys.

Ancillary Data The ancillary data used for this study were derived from a vec- tor contour map generated by digitizing topographic maps a at scale of 1:25,000 with a contour interval of 25 m. The study area covers about 1068 km2.

A contour map is a discrete representation of topographic features and, to integrate these data with spectral data, it was necessary to convert the map into a continuous format. There- fore, a raster digital elevation model (DEM) was computed by linear interpolation using an algorithm based on Borgefors distance transform (Gorte and Koolhoven, 1990). The DEM was gridded at 30 m (homogeneous to the TM image) with avertical resolution of 1 m. A shaded relief view of the DEM is shown in Figure 1.

Photogrammetric Engineering & Remote Sensing Vol. 66, No. 4, April 2000, pp. 429-435.

0099-1112/00/6504429$3.00/0 0 2000 American Society for Photogrammetry

and Remote Sensing

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Figure 1. Shaded relief view of the digital elevation model.

In addition to elevation, the different geological deposits are characterized by different landforms such as steep escarp- ment and plains. Information about landforms can be derived from the DEW, by calculating the local slope gradient along the steepest direction and terrain aspect, to produce a digital terrain model (DTM) (Burrough, 1986) . The maximum slope gradient in this area is 1 6 9 percent.

Image Processing Only the six visible and near-middle bands (1,2,3,4,5, and 7) of the image were used in this study. The TM image was geo- metrically corrected for systematic distortion (de-skewing, Earth rotation, etc.) at the receiving station, but prior to classification some processing was performed to improve the quality of the data.

Before using ancillary data to perform more sophisticated corrections, the image was corrected for haze and geocoded to the ellipsoid projection. Assuming that "path radiance" is lin- early added to the reflected radiance in the whole image (Kiefer and Lillesand, 1 9 9 3 ) , the haze correction was performed by subtracting the minimum digital number (DN) minus one i.e., (DN,,, - 1 ) from each band. The image was then geometri- cally corrected using 3 2 ground control points and an affine transformation algorithm in order to register the Landsat image and the topographic data to each other.

The northernmost and southernmost portions of the study area extend over the sea surface. Because information over the sea is irrelevant for the geological classification of the image

and can disturb the processing of the inland pixels, all sea pixels were deleted from the image. An image mask was created, by thresholding the Natural Green Vegetation Index (NGVI), in order to assign a 0 value to all sea pixels in the six TM bands.

The image derived from these basic processes was digitally classified to be used as a reference in assessing the improvements achieved by integrating with ancillary data. Figure 2 shows the geocoded TM band 5 of the study area.

More accurate image correction can be achieved by using topographic data to improve image quality before performing automatic classification. Using the DTM, spatial accuracy can be increased by correcting for relief displacement effects, and radiometric accuracy increased by normalizing shadowing effects.

Terrain Geocodlng The study area is characterized by high relief. In such areas the relief displacement inherent in remotely sensed imagery can be significant, even in imagery acquired by high altitude satellites. The relief displacement for each pixel was trigonometrically corrected using the elevation data obtained from the DEM and the satellite viewing geometry. The look angle of the Landsat satellites is 0" for the center column of the full scene, that is, column 5 5 2 in the image window selected. The resampling method used for the geometric transformation was nearest neighbor as this method does not affect the original DNs in the image.

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Figure 2. Geocoded TM image (Band 5). (O Copyright ESA 1991 - Distribuzione Eurimage, Telespazio per l'ltalia).

430 April 2000 PHOTOGRAMMWRIC ENGlNEERlNQ & REMOTE SENSING

Sun lllumlnation Correction In high relief terrain different slopes create shadows due to var- ying illumination and can be the cause of classification inaccu- racies (Drury, 1987). Moreover, the spectral pattern of rocks and soils generally contain only subtle features, which are often hidden by variations in illumination.

A correction for this effect can be made to separate the sig- nal due to ground cover from the noise caused by terrain features by applying a radiance model based on a DTM. Several authors have tested different models in order to improve the classification results of multispectral images (Jones eta]. , 1988; Proy et al., 1989; Itten and Meyer, 1993).

Both Lambertian and non-Lambertian models have been proposed to correct the radiometric effect of topography (Justice and Holben, 1979). Lambertian models assume that terrain surfaces scatter light uniformly in all directions. In this case, the normalized radiance, Ln, is equal to U(cos i), where L is the measured radiance and i is the incidence angle between the sun illumination direction and the surface normal (Figure 3).

Several non-Lambertian models have been developed for image correction, some of which were compared by Itten and Meyer (1993). To correct the TM image, the semi-empirical Min- naert reflectance model was applied (Woodham and Grey, 1986; Jones et al., 1988). This model takes into consideration the exitance angle e, between the terrain normal and the target- sensor direction, and an empirical constant, k, which is a func- tion of how much the terrain surface has a Lambertian behav- ior: i.e.,

log (L X cos e) = log Ln + k X log (cos i x cos e).

The angles used in this model are represented in Figure 3. An incidence angle was calculated for each pixel from the vectorial component of terrain surface normal and sun illumination, in the x y z directions. To estimate the illumination direction, sun elevation and sun azimuth were calculated by simple formulae (Zorn, 1982) from the day and time of image acquisition, as reported in the header of the TM image. The esti- mated values are as follows:

Sun elevation angle = 59"

Sun azimuth = 64"

The resulting vectorial position of the sun in xyz coordi- nates was calculated as S = (0.8988, -0.4384,1.6643). The

Figure 3. Parameters used in the s u n illumination model: i = incidence angle; e = exitance angle; a = s u n azimuth; 0 = sun elevation; cr = slope gradient.

slope gradients in the x and y directions were used as a vectorial component of the terrain surface normal. The k constant value for each band was set equal to the slope of the regression line of log (L cos e) against log (cos i x cos e).

Figure 4 shows the terrain geocoded TM band 5 image after radiometric correction for topographic effects. By comparing this image with the original geocoded image (Figure 2) , the nor- malization of shadowing effect achieved using this correction method can be seen, although some subtle bright and dark features are still visible in the corrected image. These features could be a consequence either of the DTM accuracy that cannot represent the more subtle topography irregularities, or of the correction method that did not take into consideration cast shadows and diffuse solar radiation (Proy et al., 1989).

Image Classification Several different procedures for geologically classifying the TM image were applied to combine spectral data and ancillary data, by employing different levels of processing.

The classification was designed to recognize the four geological formations of Plio-Quaternary age present in the study area. These deposits were represented by four classes as follows:

Class 1 Alluvial deposits

Class 2 Terrace deposits

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Figure 4. Terraincorrected image (Band 5). (O Copy- right ESA 1991 - Distribuzione Eurirnage, Telespazio per I'ltalia).

April 2000 431 I PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

Class 3 Fan conglomerates and sand

Class 4 Calcarenite and sand

These deposits are only locally outcropping in the study area; therefore, not all image pixels were classified.

The same training sample set was used for each of the different classifications performed in this study. The selection of a training set was based on field surveys and information derived from the 1:25,000-scale geological map of the area.

A spectral classification of the standard processed image and the terrain corrected image was first performed using a maximum-likelihood classifier. The results, shown in Figures 5 and 6, were used as a reference to assess the accuracy improvements obtained using ancillary data in the classification.

ClassiRcatlon Using Ancillary Data There are many examples of using ancillary data in the classification of spectral imagery. Hutchinson (1982) described three different methods of combining Landsat and ancillary data to improve digital classification accuracy. He incorporated ancillary data before, during, and after classification.

Use of ancillary data prior to classification involves a divi- sion of the study image into smaller areas or "strata" based on specific rules, such that each stratum can be processed inde- pendently. Statistically, the purpose of stratification is to increase the homogeneity of the data sets to be classified. Crite- ria selected for stratification should be signiscant in describing the variation of objects of interest within the study area.

Figure 5. Classified map of the standard-processed TM image.

Figure 6. Classified map of the terraincorrected TM image.

The simplest way to use ancillary data during classification is to increase the number of channels of information used in the classification process. This technique was termed the "logical channel" approach (Strahler et al., 1978).

Ancillary data can be used after classification to define a number of sorting rules to assign ambiguously classified pixels to an appropriate class.

Because the geomorphologic features are strictly related to the geological characteristics of Pliocene-Quaternary deposits, the addition of the slope map as a logical channel in the classification procedure was considered the most effective way to achieve improvements in classification accuracy. A maximum- likelihood classification of the standard-processed and the terrain-corrected image was performed using the logical channel approach. Their results are shown in Figures 7 and 8, respectively.

An additional classification of the terrain-corrected image was performed by employing stratification and logical channel methods. The geological deposits have distinctive characteristics, with very low slopes for alluvium and terrace deposits and higher slopes for conglomerates and calcarenites. There- fore, slope gradient was considered the most appropriate crite- rion for stratification. A threshold value equal to 12 percent was selected in the slope map to generate a mask image with all the low gradient pixels. This mask was used to stratify the TM image into two strata according to low and high slope values.

The image representing low-gradient pixels was used to classify alluvial deposits and terrace deposits (class 1 and class

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Figure 7. Classified map of the standard-processed TM image and slope map (logical channel).

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2), while the image with high-gradient pixels was used to classify fan conglomerates, and calcarenite and sand (class 3 and class 4). The slope map was additionally used as a logical channel for both strata classifications. After classification, the results of the two strata were merged into a single map, shown in Figure 9.

Classiflcation Accuracy Assessment A maximum-likelihood classification technique and the same training sample set were used for the five different classifications conducted in this study. A comparison of the classification results obtained can facilitate the evaluation of accuracy improvements achieved by integrating spectral data with ancillary data.

The results of the different classification procedures were recorded in five maps. These maps were filtered with a zero- majority filter, and then with a majority filter, in order to make the classified areas more homogeneous. The use of these filters is based on the assumption that the geological formations are distributed in homogeneous areas; it is unlikely that an image would have an isolated pixel representing a geological forma- tion different from that of its neighboring pixels.

The accuracy results of the different classification procedures were assessed using a raster representation of the geological map for a sub-area (about 312 km2) in the northwestern part of the study area. A window equal to the test map was selected from each classified map to be used for computing a confusion

560000 570000 580090

Figure 8. Classified map of the terrain-corrected TM image and slope map (logical channel).

matrix. In the confusion matrices the unclassified areas (0 values) represent other geological formations that were not taken into consideration during the classification. The resulting confusion matrices, in which every row corresponds to a geological unit in the geological map and every column corresponds to a class in the classified map, are reported in Tables 1 to 5.

The average accuracy and reliability values, although generally low, increase when the ancillary data are integrated with the spectral information of the image. The addition of ancillary data almost doubles the overall accuracy values from a minimum of 37 percent to a maximum of 62 percent. There is no significant difference between using standard-processed images and terrain-corrected images, for both lead to compara- ble classification accuracies. Furthermore, the use of the stratification method gives marginal improvement in terms of accuracy and reliability, with respect to the logical channel method, and the overall accuracy is slightly lower.

The alluvial deposits and terrace deposits, class 1 and class 2, respectively, show the highest accuracy and reliability values. These geological units are characterized by a general decrease of accuracy values with the incorporation of topographic data, with respect to the simple spectral classification, while the reliability values remarkably increase.

Conclusion This study evaluates the contribution of ancillary information to digital classification of multispectral satellite imagery for geological mapping. Although there are several limitations, the

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING April2000 433

m Class m Class

Class 3 Class 4

I Class 5

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Figure 9. Classified map of the stratified images using the slope map (logical channel).

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TABLE 3. CONFUSION MATRIX FOR CLASSIFICATION OF THE STANDARD PROCESSED TM IMAGE AND SLOPE MAP (LOGICAL CHANNEL)

class 1 class 2 class 3 class 4 class 5 accuracy

Al luvial 3163 2348 351 122 2617 0.37 Terrace 1668 18370 1096 1513 15441 0.48 Conglomerates 330 1281 525 40 305 0.21 Calcarenite 217 1584 446 748 10546 0.06 Other 477 2575 848 4824 51024 0.85 reliability 0.54 0.70 0.16 0.10 0.64

average accuracy = 39.42 963 average reliability = 42.90 % overall accuracy = 60.29 %

TABLE 4. CONFUSION MATRIX FOR CLASSIF~CATION OF THE TERRAINCORRECTED TM IMAGE AND SLOPE MAP (LOGICAL CHANNEL)



average accuracy = 37.90 % average reliability = 43.42 % overall accuracy = 62.01 %

TABLE 5. CONFUSION MATRIX FOR CLASSIFICATION OFTHE STRAT~FIED IMAGES USING THE SLOPE MAP (LOGICAL CHANNEL)



average accuracy = 38.44 % TABLE 1. CONFUSION MATRIX FOR CLASSIFICATION OFTHE STANDARD average reliability = 45.61 %

PROCESSED TM IMAGE overall accuracy = 58.59 %


Alluvial 4353 3827 128 14 384 0.50 Terrace 4389 27764 428 755 4752 0.73 results of the different classification procedures performed Conglomerates 183 2133 163 2 0 0.07 highlight the accuracy improvement attainable by integrating Calcarenite 8325 31 360 2604 Oe03 spectral and topographic information. This accuracy improve- Other reliability

15880 '05 5644 15823 OeZ6 ment is largely a result of previous knowledge about the geo- 0.16 0.44 0.10 0.05 0.67 morphologic characteristics of geological units.

average accuracy = 31.72 % The stratification method did not lead to any significant average reliability = 28.53 % improvement as differences between geological formations in overall accuracy = 39.54 % terms of slope attribute were already considered in the use of a

slope map as a logical channel. Furthermore, stratification is less sensitive to gradual differences between classes due to the

TABLE 2. CONFUSION MATRIX FOR CLASS~FICATION OF THE TERRAINCORRECTED boundaries between strata' TM IMAGE Several improvements could be made in combining ancil-

lary data and spectral data, especially in application-oriented methods. Considering that slope values do not generally follow

Alluvial 4334 3960 91 0 408 0.49 a normal distribution, a non-parametric classifier such as K Terrace 3392 29634 226 136 4700 0.78 nearest neighbor could be more suitable for classification pro- Conglomerates 174 2160 146 1 o 0.06 cedures, when using these data as logical channels. Calcarenite 1518 9392 48 156 2427 0.01 The classification results are generally poor because the Other 10404 36967 153 575 11695 0.20 geological attributes in high relief temperate areas are often reliability 0.22 0.36 0.22 0.18 0.61 spectrally not recognizable due to vegetation cover. Further- average accuracy = 30.74 % more, the digital geological map might have been affected by average reliability = 31.75 % errors in the original hardcopy map and those arising from the overall accuracy = 37.46 % digitizing procedures.

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The classification processes performed were meant to evaluate, in the most objective way, the contribution of using topographic data to improve geological image classification. Slightly better classification results could have been achieved with more attention to the statistical distribution of some training sample sets for those geological units that have no well defined spectral signature and geomorphologic characteristics.

Acknowledgment Much gratitude is extended to Rosilah Sani for his precious help in the revision of this paper. This study has been sup- ported by the National Research Council of Italy (CNR), under a research contract (No 9 7.255 .CT05) leaded by Prof. Giustino Ricchetti.

References Burrough, P.A., 1986. Principles of Geographic Lnformation Systems

for Land Resources Assessment, Clarendon Press, Oxford, UK, 194 p.

Drury, S.A., 1993. Image Interpretation in Geology, Allen & Unwin, London, UK, 243 p.

Duguay, C., G. Holder, E. LeDrew, P. Howarth, and D. Dudycha, 1989. A software package for integrating digital elevation models into the digital analysis of remote-sensing data, Computers 6. Geosci- ences, 15:669-678.

Franklin, S.E., 1989. Ancillary data input to satellite remote sensing of complex terrain phenomena, Computers b Geosciences, 15:799-808.

Franklin, S.E., and B.A. Wilson, 1991. Spatial and spectral classification of remote-sensing imagery, Computers b Geosciences, 17:1151-1172.

Gorte, B., and W. Koolhoven, 1990. Interpolation between isolines based on the Borgefors distance transform, ITC Journal, 3:245-247.

Hutchinson, C.F., 1982. Techniques for combining Landsat and ancillary data for digital classification improvement, Photogrammetric Engineering &Remote Sensing, 48:123-130.

Itten, K.I., and P. Meyer, 1993. Geometric and radiometric correction of TM data of mountainous forested areas, LEEE 7kans. on Geosc. and Remote Sensing, 31:764-770.

Jones, A.R., J.J. Settle, and B.K. Wyatt, 1988. Use of digital terrain data in the interpretation of SPOT-1 HVR multispectral imagery, Int. J. Remote Sensing, 9:668-682.

Justice, C., and B. Holben, 1979. Examination of Lambertian and Non- Lambertian Models for Simulating the Topographic Effect on Remotely Sensed Data, NASA TM 80557, NASA Goddard Space Flight Center, Greenbelt, Maryland.

Kiefer, R.W., and T.M. Lillesand, 1993. Remote Sensing and Image Interpretation, John Wiley 8r Sons, New York, 750 p.

Proy, C., D. Tanr6, and P.Y. Deschamps, 1989. Evaluation of topographic effect in remotely sensed data, Remote Sens. Environ., 30:21-32.

Strahler, A.H., T.L. Logan, and N.A. Bryant, 1978. Improving forest cover classification accuracy from Landsat by incorporating topographic information, Proc. of 2 0 ~ Inter. S p p . on Remote Sensing of Environment, pp. 927-942.

Woodham, R., and M. Grey, 1986. Analytic method for radiometric correction of satellite Multispectral Scanner data, IAPRTC7 Work- shop on Analytical Methods in Remote Sensing for Geographic Information Systems, Paris; h o l e National Sup6rieure des T616co- munications, pp. 5-53.

Zorn, H.C., 1982. A pocket calculator program for solar altitude and azimuth, ITC Journal, 4:448-449.

(Received 11 March 1999; accepted 29 April 1999; revised 27 May 1999)

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multispectral satellite image and ancillary data integration ......multispectral satellite image and...

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