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Proceedings Paper

Classification of land cover from remote sensing fused image based on ICA-SVM and D-S evidence theory
Author(s): Mi Chen; Yingchun Fu; Tao Sun; Deren Li; Qianqing Qin
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Paper Abstract

Remote sensing image classification is an important means for quantified remote sensing image analysis, and remote sensing image fusion can effectively improve the accuracy of image classification. This paper proposes a classification algorithm of remote sensing fused images based on independent component analysis (ICA), topographic independent component analysis (TICA), support vector machines (SVMs) and D-S evidence theory. Firstly a novel method of fusing panchromatic and multi-spectral remote sensing images is developed by contourlet transform which can offer a much richer set of directions and shapes than wavelet. As independent component analysis not only can effectively remove the correlation of multi-spectral images, but also can realize sparse coding of images and capture the essential edge structures and textures of images, then using features extracted from the ICA and TICA domain coefficients of the fused images, the SVMs are trained to classify the whole fused images. Finally apply the proposed novel D-S evidence combination scheme to make decision fusion for different classification results with different features obtained by SVMs. Experimental results show that the proposed algorithm can effectively improve the accuracy of image classification.

Paper Details

Date Published: 7 November 2008
PDF: 10 pages
Proc. SPIE 7147, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images, 71470E (7 November 2008); doi: 10.1117/12.813214
Show Author Affiliations
Mi Chen, Capital Normal Univ. (China)
Yingchun Fu, South China Normal Univ. (China)
Tao Sun, Wuhan Univ. (China)
Deren Li, Wuhan Univ. (China)
Qianqing Qin, Wuhan Univ. (China)


Published in SPIE Proceedings Vol. 7147:
Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images
Lin Liu; Xia Li; Kai Liu; Xinchang Zhang, Editor(s)

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