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

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.

A Research Framework for Supervised Image Classification For Tornado Chaos Phenomena

“…The training area that has been obtained is then used as an input in the classification process for the whole image [8]. The classification is monitored based on the results of the survey the identity and location of the classes are known, analysis of aerial photographs (or previous satellite images), as well as in other ways [11]. Supervised and unsupervised classifications have their own shortcomings.…”