Rs Images Classification Method For Northwest Arid Area

How to classify current land use situation based on remote sensing images,which covers a larger area with different climate and physiognomy at different phases,has gradually been a hot issue world widely in the field of land resources. This experiment to choose the northwest arid area, the six provinces of six different longitude and latitude, different ways of land use and reality, different scope, different terrain and climate of typical geomorphic units of Landsat TM remote sensing image as the research object, use a more general supervised classification methods(maximum likelihood method, BP neural network and support vector machine(SVM) method, etc.) on the Landsat TM remote sensing image classification, to improve the remote sensing image classification precision and the accuracy of the extracted land use information, in the experimental sample selection based on normalized difference vegetation index and texture characteristics such as data classification, and the classification results by adopting the method of clustering statistics and filtering analysis to classify the post-processing. Finally, using confusion matrix to evaluate the accuracy of classification result. Results show that the maximum likelihood method and BP neural network classification method, based on the normalized difference vegetation index and texture characteristics of support vector machine(SVM) method has the highest classification accuracy, topped out at 98.92%, the kappa coefficient is the highest reached 0.9771, and all the classification precision of support vector machine(SVM) method in the study area has reached more than 90%, accurately to isolate the various features. Therefore, the SVM method can be safely adopted in the further Remote sensing images classification study in similar larger area such as the Chinese Northwest Arid Area. It also can provide a methodological reference for the land resources sustainable development strategic.