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Proceedings Paper

Research on classification of hyperspectral remote sensing image based on improved NPA in SVM
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Paper Abstract

SVM (Support Vector Machine) is a new kind of machine learning method , it can solve classification and regression problems very successfully and accomplish classification with small sample incident perfectly. In this paper, the NPA is proposed to compute the optimization problem to achieve the classification for hyperspectral remote sensing (RS) image by "1 VS m" strategy and radial basis kernel function. Besides, a new method, the dual-binary tree + SVM algorithm is proposed, to solve the mutil-class, high-dimensional(HD) problems of hyperspectral RS image. In the end, the test is carried on the OMIS image. The comparative results of this algorithm with other methods are given, which shows that our algorithm is very competitive, particularly for the small samples and non-equilibrium surface features. Both the accuracy and speed of classification are improved greatly.

Paper Details

Date Published: 29 December 2008
PDF: 9 pages
Proc. SPIE 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA), 72850C (29 December 2008); doi: 10.1117/12.814992
Show Author Affiliations
Zhaoqing Shen, Wuhan Univ. (China)
Ning Shu, Wuhan Univ. (China)
Jianbin Tao, Wuhan Univ. (China)
Jie Sun, Wuhan Univ. (China)
Zulong Lai, Wuhan Univ. (China)
China Univ. of Geology (China)

Published in SPIE Proceedings Vol. 7285:
International Conference on Earth Observation Data Processing and Analysis (ICEODPA)
Deren Li; Jianya Gong; Huayi Wu, Editor(s)

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