Target-cluster fusion approach for classifying high resolution IKONOS imagery

Su, Yu-De; Huang, Ping; Lin, Chih-Min; Tu, Te-Ming

doi:10.1049/ip-vis:20040515

Cited by 8 publications

(1 citation statement)

References 26 publications

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“…High-order statistics reflect the important structure and phase feature of image. Image analysis using ICA/TICA with high-order statistics has particular advantage, it can realize sparse coding, meanwhile, ICA/TICA is excellent edge filter [7,8]. When people observe image, a series image patches are picked up firstly and then the whole image.…”

Section: (Topographic) Independent Component Analysismentioning

confidence: 99%

Classification of land cover from remote sensing fused image based on ICA-SVM and D-S evidence theory

Sun

et al. 2008

SPIE Proceedings

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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.

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Section: (Topographic) Independent Component Analysismentioning

confidence: 99%