关键词: 遥感图像, 林地类型分类, 纹理特征, 独立成分分析, 支持向量机

Abstract: Remote sensing texture can be used to correctly classify forest species whose spectrums are similar. However, texture results in increase of the feature vector dimension and computation. Hence, TM forest image is classified with Support Vector Machine（SVM） based on texture of gray level co-occurrence matrix after it is decreasing dimension with Independent Component Analysis（ICA）. Results show that forest species can be classified correctly with the algorithm as its classification accuracy is 85.4% and Kappa coefficient is 0.73, which are greater than those with SVM, BP neural network, maximum likelihood and minimum distance algorithms. The accuracies to classify broadleaf, conifer and bamboo are 78.2%, 80.1% and 84.3%. Errors mainly result from mixed forests and intersection between two forest types. The classification accuracies of broadleaf, conifer are lower than that of bamboo because of easy appearance of broadleaf and conifer mixed forests.

Key words: remote sensing image, forest classification, texture, Independent Component Analysis（ICA）, Support Vector Machine（SVM）