Abstract
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.
Highlights
1.1 Integration of optical and Synthetic Aperture Radar (SAR)Synergistic uses of different kind of remote sensing data, 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
The overall classification accuracy for the Support Vector Machines (SVMs), Artificial Neural Network (ANN) and Self-Organizing Map (SOM) classifier over different datasets using feature selection and non-feature selection approach is summarised in the table 3
As for the non-feature selection (FS) approach the combined multi-date Landsat 5 TM+ and SAR data increase overall accuracy by 2.46% and 22.1% for SVM, 5.5% and 21.6% for ANN and 0.06% and 23.97% for SOM compared to the cases that only multi-date Landsat 5 TM+ or SAR images was used
Summary
Synergistic uses of different kind of remote sensing data, 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. Use of multiple types of remote sensing data has high potential to increase the classification accuracy it makes data volume increase rapidly with large amount of highly correlated features and redundant information. Employing large data volume does not always result in an increase in classification accuracy. In contrary, it will increase uncertainty within dataset and could reduce classification accuracy significantly. The challenging task is how to select optimally combined datasets which give the best classification
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