performance of commercial and open source remote sensing

Performance of commercial and open source remote sensing/image

processing software for land cover/use purposes

Ana C. Teodoro*a,b, Dário Ferreirab, Neftali Silleroa

aGeo-Space Sciences Research Center, Faculty of Sciences, University of Porto (CICGE/FCUP),

Rua do Campo Alegre, s/n, 4169-007 Porto, Portugal; bDep. of Geosciences, Environment and Land Planning, Faculty of Sciences, University of Porto,

Rua do Campo Alegre, s/n, 4169-007 Porto, Portugal

ABSTRACT

We aim to compare the potentialities of four remote sensing/image processing software: PCI Geomatica V8.2, ENVI 4.7, SPRING 5.1.8, and ORFEO toolbox integrated in Monteverdi 1.11. We listed and assessed the performance of several classification algorithms. PCI Geomatica and ENVI are commercial/proprietary software and SPRING and ORFEO are open source software. We listed the main classification algorithms available in these four software, and divided them by the different types/approaches of classification (e.g., pixel-based, object-oriented, and data mining algorithms). We classified using these algorithms two images covering the same area (Porto-Vila Nova de Gaia, Northern Portugal): one Landsat TM image from October 2011 and one IKONOS image from September 2005. We compared time of performance and classification results using the confusion matrix (overall accuracy) and Kappa statistics. The algorithms tested presented different classification results according to the software used. In Landsat image, differences are greater than IKONOS image. This work could be very important for other researchers as it provides a qualitative and quantitative analysis of different image processing algorithms available in commercial and open source software.

Keywords: Classification algorithms, open source software, commercial/proprietary software, algorithms performance, Landsat, IKONOS, land cover/use

1. INTRODUCTION Remote sensing is a powerful tool for the exploration of the Earth surface since it provides a synoptic view of an area with a high temporal resolution [1]. The limitations associated with geographical data and their analysis (e.g. spatial resolution [2] and the definition of thematic classes [3]), the cost of remote sensing or GIS software as well as imagery, can hamper their acquisition by researchers [1]. Therefore, the development of free tools for robustly processing remotely sensed data could, therefore, provide researchers with a valuable resource for environmental analysis.

Currently, several remote sensing data analysis techniques still require human intervention. In this sense, some commercial/proprietary software packages for the automatic interpretation of images have been developed in the last decades, aiming to overcome the drawbacks imposed by conventional classifiers. Although this new generation of programs represents a considerable advance in relation to the conventional classifiers, some important challenges remain in the domain of automatic interpretation of images, such as to assure a greater accuracy and detailing capacity in feature extraction and in classification.

The development of free and open source software has experienced a boost over the past few years. Figure 1 provides a structured overview on the commonly used terms. The terms ‘free software’ and ‘open sources software’ are slightly different (see above).

*[email protected]; phone 00351 220402470; fax 00351220402490

Earth Resources and Environmental Remote Sensing/GIS Applications III, edited by Daniel L. Civco, Manfred Ehlers,Shahid Habib, Antonino Maltese, David Messinger, Ulrich Michel, Konstantinos G. Nikolakopoulos, Karsten Schulz,

Proc. of SPIE Vol. 8538, 85381K • © 2012 SPIE • CCC code: 0277-786/12/$18 • doi: 10.1117/12.974577

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free software

public domain software(with source)

software underlax permissive license

copylefted software

software under GPL

open source software

proprietary software

public domain software

(without source)

shareware

free-download software

Opposite to them is the ‘proprietary software’ [5], enclosing terms such as ‘closed’ software and ‘shareware’. The concept of ‘free software’ was established by the Free Software Foundation (FSF, www.fsf.org) and for the one of ‘open source software’ by the Open Source Initiative. According to the FSF, software can be labelled as free if the associated license conditions fulfil the ‘Free Software Definition’, which grants four freedoms:

(1) The freedom to run the program for any purpose;

(2) The freedom to study how the program works and adapt it to your needs;

(3) The freedom to redistribute copies so you can help your neighbour;

(4) The freedom to improve the program, and to release your improvements to the public, so that the whole community benefits.

Often the terms ‘open source’ and ‘free’ software are used synonymously. However, open source software provide a total and free access to the source code, that can studied by the user. Even so, it does not encompass the freedoms of modification and redistribution; therefore, the term ‘free/open source software (FOSS)’ is more appropriate to describe software that fulfils the above-mentioned four conditions [5]. Software can be certified by the Open Source Initiative (OSI) as being ‘open source’ [6].

Figure 1. Terms used with respect to software licenses (available: www.fsf. org/licensing/essays/categories.html).

The main objective of this work was to compare the potentialities and list the available classifications algorithms of four remote sensing/image processing software: two commercial/proprietary software (PCI Geomatica and ENVI) and two open source software (SPRING and ORFEO).

2. METODOLOGHY In order to compare the functionalities/potentialities of different remote sensing/image processing software, we analysed two commercial/proprietary software and the two open source software. There are several commercial/proprietary software very powerful and widely used. We focused our work in PCI Geomatica 8.2 and ENVI 4.7, because these are the educational software available in our institution.

The open source available software in remote sensing/image processing area is not as wide as in other areas (e.g., in GIS), and only in recent years had significant developments. There are still some image processing algorithms included in open source GIS software (e.g., GRASS (http://grass.fbk.eu)).



In the current scenario of the open source software available for this area, and considering the authors' previous experience, were selected SPRING and ORFEO software. Other open source software may also be considered, such Opticks (http://opticks.org).

2.1 Software tested

PCI Geomatics was founded in 1982, and has set the standard in remote sensing and image processing tools offering customized solutions to the geomatics community in over 135 countries. PCI Geomatics is the developer of Geomatica®- a complete and integrated desktop software that features tools for remote sensing, digital photogrammetry, geospatial analysis, map production, mosaicking and more [8]. PCI Geomatics developed an easy-to-use and complete software that addresses user needs for producing high-quality 2D and 3D geospatial information for GIS, CAD, and mapping applications. PCI Geomatica includes different software packages as ImageWorks, EASI/PACE or GCPWorks. As already referred, PCI Geomatica is a commercial software.

ENVI is a software for the visualization, analysis, and presentation of all types of digital imagery. ENVI’s complete image-processing package includes advanced, yet easy-to-use, spectral tools, geometric correction, terrain analysis, radar analysis, raster and vector GIS capabilities, extensive support for images from a wide variety of sources, and much more. ENVI’s approach to image processing combines file-based and band-based techniques with interactive functions. ENVI’s interface is complemented by its comprehensive library of processing algorithms. It includes all the basic image processing functions, plus input of non-standard data types, viewing and analysis of large images, and simple extensions of analysis capabilities (add-on functions). The software includes essential tools required for image processing across multiple disciplines, and it has the flexibility to allow implementation of customized analysis strategies [9]. ITT Visual Information Solutions offers several add-on modules to extend ENVI’s functionality (e.g., DEM Extraction Module, Orthorectification Module or Atmospheric Correction Module). Each module requires an additional license. ENVI, as PCI Geomatica, is a commercial software.

SPRING is a state-of-the-art open source GIS and remote sensing image processing system with an object-oriented data model which provides for the integration of raster and vector data representations in a single environment [10]. SPRING is a product of Brazil's National Institute for Space Research (INPE/DPI (Image Processing Division). The SPRING main features are:

(1) An integrated GIS for environmental, socioeconomic and urban planning applications;

(2) A multi-platform system, including support for Windows95/98/NT/XP and Linux;

(3) A widely accessible freeware for the GIS community with a quick learning curve;

(4) To be a mechanism of diffusion of the knowledge developed for the INPE and its partners with the introduction of new algorithms and methodologies.

ORFEO Toolbox is distributed as an open source library of image processing algorithms adapted to large remote sensing images, developed to the French Space Agency (CNES). ORFEO encourages full access to the details of all the algorithms. Targeted algorithms for high resolution optical images, hyperspectral sensors or SAR are available [11]. ORFEO is distributed under a free software license CeCILL (similar to GPL) to encourage contribution from users and to promote reproducible research. The library is intensively tested on several platforms as Linux, Unix and Windows. Among other, ORFEO provides a number of heavily documented functionalities as:

(1) Standard remote sensing preprocessing: radiometric corrections, orthorectification;

(2) Filtering and feature extraction;

(3) Image segmentation, image classification and change detection.

2.2 Data, study area and classes definition criteria

In this study, we used one Landsat 5 TM image from 2011/10/05 and one IKONOS image from 2005/09/18 (Fig. 2). The Landsat 5 TM and IKONOS images were already geometrically corrected (UTM projection zone 29N and WGS84 datum) and predominantly cloud-free. The visible bands (bands 1-3) and the near infrared band (band 4) of both images



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Table 2. Classes defined and the correspondent Corine land cover nomenclature.

First Level Second level Third level

Artificial surfaces Urban fabric Discontinuous urban fabric (URB)

Artificial surfaces Industrial, commercial and transport units Industrial or commercial units and roads ( ICR)

Forests and semi-natural areas Shrub and/or herbaceous vegetation association Vegetation* (VEG)

Forests and semi-natural areas Open spaces with little or no vegetation Beaches, dunes and sand plains (SND)

Water bodies Inland and coastal wetlands water bodies* (WTR)

*super-classes

2.3 Image classification processes

Before performing the classification, the signature separability function must be computed in order to examine the quality of training site and class signature. The signature separability contains all the available information about signature and information for each class. The “Bhattacharrya Distance” measures are real values between “0” and “2”, where “0” indicates complete overlapping between the signatures of two classes and “2” indicates a complete separation between the two classes. Both measures are monotonically related to classification accuracies. The larger the separability values are, the better the final classification results will be.

We recorded processing time and accuracy of each algorithm in order to compare their performance in the four software. These procedures were performed in the same environment: same computer (Acer-Aspire 5930, Intel® Core™ 2 DUO CPU P7350 @ 2.00GHz, 3.00 GHz RAM) and same operating system (Windows Vista ™ Home Premium 6.0).

Land cover and use maps are of paramount importance in various applications such as land monitoring, land use planning, hydrological modelling and natural resource management. Several types of classification algorithms are available (e.g. pixel-based, object-oriented, segmentation, etc). The pixel-based was one of the first types to be created. In this paper was used pixel-based method, specifically the K-Means (unsupervised), Parallelepiped, Minimum distance and Maximum likelihood (supervised). These classifiers are widely accepted in remote sensing.

We computed two statistics variables in order to assess the classification results: overall accuracy (OA) and kappa coefficient (k). The overall accuracy is defined by the ratio between the total number of correct classification and total number of classifications, and is given in 0-100% range. The closer to 100% the better is the percentage of correct classifications. The kappa coefficient is a measure of proportional improvement by the classifier over purely random assignment to classes. The kappa coefficient takes values from “0” to “1”, where “0” means that classification result is not better than a random assignment of pixels, and k=1 means that the classification result was 100% better than the result obtained by probability.

Based on classification result images, land cover maps were generated (Fig. 3 and 4). The size distribution classes were recorded for each algorithm.

3. RESULTS 3.1 Algorithms available

A list with different classification algorithms available in the analyzed versions of these four software are presented in the Table 3.



Table 3. Classification algorithms available in the software investigated.

Method Algorithm Software

PCI ENVI ORFEO SPRING

Pixel-based

unsupervised

k-means x x x x

Fuzzy K-means x

K-Nearest Neighbour x

IsoData x x

supervised

Maximum likelihood x x x

Maximum likelihood1 x

Maximum likelihood2 x

Parallelepiped x x

Parallelipeped3 x

Minimum distance x x x

Spectral angle mapper x x

Spectral information divergence x

Binary encoding x

Mahalanobis distance x

Markov random fields x

Segmentation

Region growing x x

Watershed x x

Level sets x

Functional minimizing x4

Object-oriented

Isoseg x

Battacharya x

ClaTex x

Data mining

Decision Tree x

Neural Networks x5 x

Support Vector Machine x x

Principal Components PCA x x x 1 Interated Conditional Modes

2 With null class

3 With Maximum Likelihood as tie breaker

4 In ENVI Feature Extraction Module

5 AVG Texture; Fuzzy and Context algorithms



3.2 Classes separability

In the case of the Landsat image (Table 4), only the separability between “Discontinous urban fabric” and “Sand” classes was poor (<1.9). Several classes presented maximum separability (2.000000), and the average separability was 1.985734.

Table 4: Signature separability values (“Bhattacharrya Distance”) for Landsat data.

Class WTR URB SND ICR

URB 1.999926

SND 2.000000 1.881598

ICR 2.000000 2.000000 2.000000

VEG 1.977404 1.999994 1.998419 2.000000

In the case of the IKONOS image (Table 5), all the classes presented a good separability (>1.9). Several classes presented maximum separability (2.000000). The minimum separability value was found for “Water” and “Vegetation” classes (1.963295), and the average separability was similar to the Landsat data (1.989591).

Table 5: Signature separability values (“Bhattacharrya Distance”) for IKONOS data.

Class WTR URB SND ICR

URB 1.999169

SND 2.000000 1.964114

ICR 2.000000 1.977718 2.000000

VEG 1.963295 1.996063 2.000000 1.995555

3.3 Algorithms performance

The time expended to process Landsat images is lesser than IKONOS image (Table 6). In the IKONOS image, the time required for the same algorithm was very distinct in each software. The biggest difference (28.79 seconds) came from Maximum likelihood algorithm, between PCI Geomatica and SPRING software. The same scenario occurred in K-Means algorithm, where the K-Means in ORFEO differed in 21.49 seconds to the same algorithm in PCI Geomatica. In both cases, PCI Geomatica took less time for processing the image. In Landsat image processing, the differences were lower than IKONOS image. The main difference (18.65 seconds) happened between Maximum likelihood algorithm in SPRING and PCI Geomatica.

Most of algorithms presented very good accuracy with an overall accuracy=100.00% and kappa=1.00000 (Table 7). An exception was the low accuracy of Minimum distance classifier in IKONOS image processing (OA=83.33, k=0.78522). For K-Means algorithm was not showed the statistics variables in Table 7 because overall accuracy and kappa coefficient were computed only for supervised algorithms. Based on classification result images, land cover maps were generated.

In Figure 3 and Figure 4 are present land cover maps produced by two of the four algorithms tested in considered software’s.



Table 6: Algorithms time requirements.

Time (seconds)

Algorithm Image PCI Geomatica ENVI ORFEO SPRING

K-Means IKONOS 2.55 10.22 24.04 8.19

Landsat 2.21 2.38 3.56 4.48

Parallelepiped IKONOS 0.74 1.71 NA NA

Landsat 0.70 0.95 NA NA

Minimum distance

IKONOS 0.42 2.48 NA 7.87

Landsat 0.41 0.79 NA 2.34

Maximum Likelihood

IKONOS 0.64 3.75 NA 29.43

Landsat 0.58 0.96 NA 9.23 *NA: Not available in software

Table 7: Algorithms accuracy assessment.

PCI Geomatica ENVI ORFEO SPRING

Algorithm Image OA kappa OA kappa OA kappa OA kappa

K-Means IKONOS - - - - - - - -

Landsat - - - - - - - -

Parallelepiped IKONOS 97.92 0.97205 100.00 1.00000 - - - -

Landsat 100.00 1.00000 100.00 1.00000 - - - -

Minimum distance IKONOS 83.33 0.78522 100.00 1.00000 - - 100.00 1.00000

Landsat 98.00 0.97329 100.00 1.00000 - - 100.00 1.00000

Maximum Likelihood

IKONOS 99.12 0.98823 100.00 1.00000 - - 100.00 1.00000

Landsat 100.00 1.00000 100.00 1.00000 - - 100.00 1.00000

In the case of the IKONOS image, the K-Means algorithm showed identical results over the four software, with an exception of Industrial or commercial units or roads (ICR), where the difference between the class in ENVI and PCI Geomatica software was 6.56% of full area of image (8.9 km2). The Parallelepiped algorithm was present only in PCI Geomatica and ENVI. This algorithm represented the biggest difference between the results of software in all classes. In ENVI software the classifier did not classify any pixel as Water bodies (WTR), despite the training sites included water cluster. In Discontinuous urban fabric (URB) class the difference achieved 30.29% (41.2 km2). In the same classifier 6.49% (8.8 km2) were classify as Null class in both software’s. Considering the four algorithms tested, the minimum distance was the one that showed results more similar in all classes, and did not classify any pixel as Null. The results of Maximum likelihood algorithm practically did not have differences in WTR and ICR classes. However, in URB class the result in ENVI and SPRING software differed approximately 10% of full area of IKONOS image (13.7 km2).



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Figure 3. Landsat classification result image by K-Means classifier, in software’s assessed in this work.

In Landsat image processing (Fig. 6) the K-Means, Parallelepiped, Minimum distance and Maximum likelihood algorithms generated different classification result in each software assessed. The K-Means classifier created practically the same dimension of WTR and URB class in all software. Nevertheless, the variation between software increased in SND,VEG and ICR class. In fact, this classifier present in ORFEO did not classify any pixel as ICR. The larger divergences occurred in SND class between the ENVI and PCI Geomatica (23.5 km2), and in VEG class between the same software (14.6 km2). In the Parallelepiped algorithm, percentage (61.54%) of area classified as SND in ENVI software was excessive based on photointerpretation and our personal knowledge of study area. This classifier present in ENVI did not produced WTR and URB classes. In PCI Geomatica, 21.8% of full area of Landsat image was classify as Null. The more regular results in all software were presented by Minimum distance algorithm. Despite that, the difference between PCI Geomatica and ENVI in VEG class was 6.92% (9.4 km2). The Maximum likelihood made several peaks in WTR and SND classes. In PCI Geomatica the WTR class filled about 30% of study area. The ENVI and SPRING produced the SND class much large that would be expected, with 69.8 km2 and 53.3 km2 respectively.



Figure 4. IKONOS classification result produced by Maximum likelihood algorithm, in PCI Geomatica, ENVI and SPRING

software.

4. DISCUSSION AND CONCLUSIONS We expected that one classifier produced the same classification result (dimension of classes) in all software, when the training sites are exactly the same. Analyzing the results for IKONOS image processing in Figure 5 can be verified that is not true.

Figure 5. Dimension of each class divided into the four algorithms tested: K-Means (K-M), Parallelepiped (PRLL),

Minimum distance (MIN) and Maximum likelihood (MAX), in PCI Geomatica, ENVI, ORFEO and SPRING software’s for IKONOS image.

0

10

20

30

40

50

60

70

K-M

PRLL

MIN

MA

X

K-M

PRLL

MIN

MA

X

K-M

PRLL

MIN

MA

X

K-M

PRLL

MIN

MA

X

K-M

PRLL

MIN

MA

X

K-M

PRLL

MIN

MA

X

WTR URB SND ICR VEG Null

% of full area of IKONOS

image

Clusters/Algorithms

IKONOS Image PCI

Envi

Orfeo

Spring



Figure 6. Dimension of each class divided into the four algorithms tested: K-Means (K-M), Parallelepiped (PRLL),

Minimum distance (MIN) and Maximum likelihood (MAX), in PCI Geomatica, ENVI, ORFEO and SPRING software’s for Landsat image.

Comparing the results of both images, the differences of performance of the same algorithm in different software was higher in Landsat image. The Landsat image represents more ground objects in same pixel. Algorithms use the same mathematical models and statistics variables to achieve the classification image. However, the stop criteria value of looping’s or decision criteria values can vary software to software. For that reason, the final object classes may have several proportions in different software.

In this work, the images were cut into a small region of interest. In works that use temporal series of images with large sizes the choice of algorithms and software to process them should into to account the time requirements to maximize the efficiency of processing task. At the moment of plan the work, the users/researchers also should consider that the final results obtained could be different if work with other software.

In the future, this work could be complemented with fields survey in order to increasing the accuracy of the results and identify which classes in which software’s present the best result.

ACKNOWLEDGEMENTS

The authors would also like to express their acknowledgement to U.S. Geological Survey (USGS), Earth Resources Observation & Science Center (EROS) and from Global Land Cover Facility, University of Maryland for provide Landsat data and to European Space Agency (ESA) for providing IKONOS image.

REFERENCES

[1] Gillespie, T.W., Foody, G.M., Rocchini, D., Giorgi, A.P. and Saatchi, S., “Measuring and modeling biodiversity from space,” Progress in Physical Geography 32, 203-221 (2008).

[2] Jelinski, D.E. and Wu, J., “The modifiable areal unit problem and implications for landscape ecology,” Landscape Ecology 11, 129-140 (1996).

0

10

20

30

40

50

60

70

K-M

PRLL

MIN

MA

X

K-M

PRLL

MIN

MA

X

K-M

PRLL

MIN

MA

X

K-M

PRLL

MIN

MA

X

K-M

PRLL

MIN

MA

X

K-M

PRLL

MIN

MA

X

WTR URB SND ICR VEG Null

% of full area of Landsat

image

Clusters/Algorithms

Landsat Image PCI

Envi

Orfeo

Spring



[3] Ricotta, C, “On possible measures for evaluating the degree of uncertainty of fuzzy thematic maps,” International Journal of Remote Sensing 26, 5573-5583 (2005).

[4] Ehlers, M., Janowsky, R. and Gähler, M., “New remote sensing concepts for environmental monitoring,” Proc. of the conference on remote sensing for environmental monitoring, GIS applications, and geology, Toulouse, France, 1-12 (2002).

[5] Steiniger, S. and Bocher, E., “An overview on current free and open source desktop GIS developments,” International Journal of Geographical Information Science, 23, 1345-1370 (2009).

[6] Open Source Initiative, “Open Source Iniciative,” <http://www.opensource.org/> (20 February 2012). [7] Warner, T. and Campagna, D. J., [Remote Sensing with IDRISI Taiga: A Beginner's Guide], Geocarto

International Centre, Hong Kong, 298 pages (2009). [8] PCI Geomatics , “PCI Geomatics,”< http://www.pcigeomatics.com > (22 February 2012). [9] Getting Started with ENVI, [ENVI Versions 4.7], ITT Visual Information Solutions (2009). [10] Camara G., Souza R.C.M., Freitas U.M. and Garrido J., "SPRING: Integrating remote sensing and GIS by

object-oriented data modelling," Computers & Graphics, 20:(3) 395-403 (1996). [11] ORFEO Toolbox, “ORFEO Tollbox,”< http://www.orfeo-toolbox.org > (5 January 2012). [12] Painho, M. and Caetano, M., [Cartografia de ocupação do solo Portugal continental 1985-2000-CORINE Land

Cover 2000]. Instituto do Ambiente (Eds), ISBN: 972-8577-27-3 (2005). [13] Corine land cover update 2000, [Technical report]. EEA European Environment Agency (2002).



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