Combination of Genetic Algorithm and Dempster-Shafer Theory of Evidence for Land Cover Classification Using Integration of SAR and Optical Satellite Imagery

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]  Qihao Weng,et al.  A survey of image classification methods and techniques for improving classification performance , 2007 .

[2]  Wei Zhang,et al.  Multiple Classifier Combination for Hyperspectral Remote Sensing Image Classification , 2009, MCS.

[3]  Lorenzo Bruzzone,et al.  An advanced system for the automatic classification of multitemporal SAR images , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[4]  John Trinder,et al.  COMBINING STATISTICAL AND NEURAL CLASSIFIERS USING DEMPSTER-SHAFER THEORY OF EVIDENCE FOR IMPROVED BUILDING DETECTION , 2010 .

[5]  Arjun Sheoran,et al.  Land Cover/Use Classification Using Optical and Quad Polarization Radar Imagery , 2009 .

[6]  Sangbum Lee,et al.  Subpixel analysis of Landsat ETM/sup +/ using self-organizing map (SOM) neural networks for urban land cover characterization , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Paul M. Mather,et al.  The use of backpropagating artificial neural networks in land cover classification , 2003 .

[8]  Jon Atli Benediktsson,et al.  Fusion of Support Vector Machines for Classification of Multisensor Data , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Jon Atli Benediktsson,et al.  Multiple Classifier Systems in Remote Sensing: From Basics to Recent Developments , 2007, MCS.

[10]  I. Woodhouse,et al.  Land-cover classification using radar and optical images: a case study in Central Mexico , 2010 .

[11]  John Trinder,et al.  Evaluation of the self‐organizing map classifier for building detection from lidar data and multispectral aerial images , 2009 .

[12]  Cheng-Lung Huang,et al.  A GA-based feature selection and parameters optimizationfor support vector machines , 2006, Expert Syst. Appl..

[14]  Barnali M. Dixon,et al.  Multispectral landuse classification using neural networks and support vector machines: one or the other, or both? , 2008 .

[15]  Zhigang Chen,et al.  Classification of hyperspectral remote sensing image based on genetic algorithm and SVM , 2010, Optical Engineering + Applications.

[16]  André Twele,et al.  Regional land cover mapping in the humid tropics using combined optical and SAR satellite data—a case study from Central Sulawesi, Indonesia , 2009 .

[17]  Taskin Kavzoglu,et al.  A kernel functions analysis for support vector machines for land cover classification , 2009, Int. J. Appl. Earth Obs. Geoinformation.