关键词: 森林林型分类, 遥感, 支持向量机（SVM）, 多特征选择, 小波变换

Abstract: In order to study the impact of multi-feature selection on remote sensing forest species classification with SVM, texture features at differrent scales of wavelet transform, four vegetation indexes and optimum band spectral features are selected to make up classification multi-feature vectors. Results show that the forest species classification accuracies with texture features, vegetation indexes and optimum band spectral features are the highest. They are respectively 84.4%, 86.5% and 91.0% for broadleaf, conifer and bamboo, 4.1%, 4.0%, 1.1% higher than those with only texture features, and 9.2%, 11.8%, 11.9% higher than those with only vegetation indexes. Generally speaking, the classification accuracies with multi-features are higher than those with single feature. Texture features in the multi-feature vectors could improve forest species separability obviously, and vegetation indexes have certain effectiveness. However, optimum band spectral features show weak effects on the raise of forest species classification accuracies.

Key words: forest species classification, remote sensing, Support Vector Machine（SVM）, multi-feature selection, wavelet transform