Salt-affected soils classification using remotely sensed images is one of the most common applications in remote sensing,and many algorithms have been developed and applied for this purpose in the literature.This study takes the Delta Oasis of Weigan and Kuqa Rivers as a study area and discusses the prediction of soil salinization from ETM +Landsat data.It reports the Support Vector Machine(SVM) classification method based on Independent Component Analysis(ICA) and Texture features.Meanwhile,the letter introduces the fundamental theory of SVM algorithm and ICA,and 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 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 98.64%,which increases by10.2% compared with maximum likelihood classification,even increases by 12.94% compared with neural net classification,and thus acquires good effectiveness.Therefore,the classification method based on SVM and incorporating the ICA and texture features can be adapted to RS image classification and monitoring of soil salinization. Salt-affected soils classification using remotely sensed images is one of the most common applications in remote sensing, and many algorithms have been developed and applied for this purpose in the literature. This study takes the Delta Oasis of Weigan and Kuqa Rivers as a study area and discusses the prediction of soil salinization from ETM + Landsat data. It reports the Support Vector Machine (SVM) classification method based on Independent Component Analysis (ICA) and Texture features. Meanwhile, the letter introduces the fundamental theory of SVM algorithm and ICA, and 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 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 a bility toward higher array input. The overall accuracy is 98.64%, which increases by 10.2% compared with maximum likelihood classification, even increases by 12.94% compared with neural net classification, and the beneficiary good effectiveness.Therefore, the classification method based on SVM and incorporating the ICA and texture features can be adapted to RS image classification and monitoring of soil salinization.