Research on Classification of Hyperspectral Remote Sensing Image Based on Improved NPA in SVM

摘要

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 " 1VS 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.