XXII ISPRS Congress 2012: Technical Commission VII

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Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B7)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VII/4: METHODS FOR LAND COVER CLASSIFICATION]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: COMBINATION OF GENETIC ALGORITHM AND DEMPSTER-SHAFER THEORY OF EVIDENCE FOR LAND COVER CLASSIFICATION USING INTEGRATION OF SAR AND OPTICAL SATELLITE IMAGERY H. T. Chu and L. Ge

Document type:: Multivolume work

Structure type:: Chapter

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia COMBINATION OF GENETIC ALGORITHM AND DEMPSTER-SHAFER THEORY OF EVIDENCE FOR LAND COVER CLASSIFICATION USING INTEGRATION OF SAR AND OPTICAL SATELLITE IMAGERY H. T. Chu' and L. Ge School of Surveying and Spatial Information Systems, The University of New South Wales, Sydney NSW 2052, AUSTRALIA Ph. +61 2 93854174, Fax. *61 2 9313 7493 ht.chu@student.unsw.edu.au Commission VII, Working Group VII/4 KEY WORDS: Accuracy, Classification, Feature, Integration, Land Cover, Landsat, SAR, Texture ABSTRACT: The integration of different kinds of remotely sensed data, in particular Synthetic Aperture Radar (SAR) and optical satellite imagery, is considered a promising approach for land cover classification because of the complimentary properties of each data source. However, the challenges are: how to fully exploit the capabilities of these multiple data sources, which combined datasets should be used and which data processing and classification techniques are most appropriate in order to achieve the best results. In this paper an approach, in which synergistic use of a feature selection (FS) methods with Genetic Algorithm (GA) and multiple classifiers combination based on Dempster-Shafer Theory of Evidence, is proposed and evaluated for classifying land cover features in New South Wales, Australia. Multi-date SAR data, including ALOS/PALSAR, ENVISAT/ASAR and optical (Landsat 5 TM+) images, were used for this study. Textural information were also derived and integrated with the original images. Various combined datasets were generated for classification. Three classifiers, namely Artificial Neural Network (ANN), Support Vector Machines (SVMs) and Self-Organizing Map (SOM) were employed. Firstly, feature selection using GA was applied for each classifier and dataset to determine the optimal input features and parameters. Then the results of three classifiers on particular datasets were combined using the Dempster-Shafer theory of Evidence. Results of this study demonstrate the advantages of the proposed method for land cover mapping using complex datasets. It is revealed that the use of GA in conjunction with the Dempster-Shafer Theory of Evidence can significantly improve the classification accuracy. Furthermore, integration of SAR and optical data often outperform single-type datasets. 1. INTRODUCTION 1.1 Integration of optical and SAR Synergistic uses of different kind of remote sensing data, particularly, multispectral and SAR imagery for land cover classification has become an attractive research area since advantages of each kind of data sources can be integrated together in order to enhance the classification performance. Many studies based on the combination approach using different datasets and classification techniques have been conducted (e.g. Erasmi and Twelve 2009, Sheoran et al. 2009, Chu and Ge 2010, Ruiz et al. 2010). Most authors reported that the integration of multiple types of data has led to improvement in classification performance. Although use of multiple types of remote sensing data has high potential to increase the classification accuracy it also makes data volume increase rapidly with large amount of highly correlated features and redundant information. Unfortunately, employing large data volume does not always result in an increase in classification accuracy. In contrary, it will also increase uncertainty within dataset and could reduce classification accuracy significantly. According to Kavzoglu and Mather (2003) a large amount of data inputs decreases generalisation capabilities of the classifiers and produce more redundant and irrelevant data. Lu and Weng (2007) also pointed * Corresponding author. out that utilisation of too much input data may not improve (but can actually decrease) the classification accuracy, and it is important to select only input variables that are useful for discriminating land cover classes. Hence, the challenging task is how to select optimally combined datasets which give the best classification. The Feature Selection (FS) techniques often employed to search for optimal or nearly optimal input datasets. Many FS methods have been used in remote sensing such as exhaustive search, forward and backward sequential feature selection, simulated annealing and Genetic Algorithm (GA). Numerous studies have shown that the GA technique is very efficient in dealing with large datasets and has a larger chance to avoid a local optimal solution than other methods (Huang et al. 2006, Zhou et al. 2010). Another advantage of the GA techniques is its capability to search for input features and parameters of classifier simultaneously. 1.2. Classification techniques Applying appropriate classification algorithms is also very important for land cover classification. The traditional parametric classification algorithms such as Minimum Distance, Maximum Likelihood (ML) classifiers have been used widely to classify remote sensing images. These classifiers can produce relatively good classification results in rather short time.

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