关键词: 独立成分分析, 纹理特征, 支持向量机, 盐渍地, 分类

Abstract: This paper takes the Delta Oasis of Weigan and Kuqa rivers for example，using ETM+data，discusses the extraction method of soil salinization.This paper reports the SVM’s classification method based on Independent Component Analysis（ICA） and Texture features（ICA-T-SVM method）.Meanwhile，the paper introduces the fundamental theory of SVM algorithm and ICA，then incorporates ICA and texture features.The classification result is compared with ICA-SVM classification，single data source SVM classification，Maximum Likelihood Classification（MLC） and neural network classification qualitatively and quantitatively.The research result shows that this method can effectively solve the problem of low accuracy and fracture classification result in single data source classification.It has high spread ability toward higher array input.The overall accuracy is 93.4183%，which increases by 3.4123% compared with SVM based on ICA and increases by 3.4237% compared with single data source SVM classification，and increases by 4.9796% compared with maximum likelihood classification，even increases by 7.7144% compared with neural network classification，and thus acquires good effectiveness.The classification results are easier interpreted when compared with the conventional classification method.Therefore，the classification method based on SVM（Support Vector Machine） and incorporating the ICA and texture features can be adapted to RS image classification and monitoring of soil salinization，furthermore，provides effective way for the things remote sensing information extraction.

Key words: Independent Component Analysis（ICA）, texture features, Support Vector Machines（SVM）, soil salinization, classification