Research on Classification of Remote Sensing Image Based on SVM Including Textural Features

Abstract

In order to solve the problems in the traditional remote sensing image based on spectral information, such as low classification accuracy, different object with the same spectral features or the same object with the different spectral features, and limited sample quantity and so on, a remote sensing image classification method based on the support vector machine (SVM) including with textural features is proposed. Using Langqi Island of Fuzhou as experimental area, preprocessing and principal component analysis were made to initialize TM images, and the spectral features and GLCM-based textural features of ground objects were extracted and analyzed respectively. Then, the extraction, training and testing of samples based on the two types of features were finished for training various SVM classifiers, which were used for classifying land use in the experimental area. Through the maximum likelihood method, the BP neural network and the support vector machine (SVM), a crossed classification and contrast experiment was made to two different types of samples based on the simple spectral features and the features combined with texture respectively. The experimental results showed that the SVM classification method including textural features can effectively improve the accuracy of land use classification, and therefore it can be promoted better.