CAJ | 학술논문

运用SPOT-5全色和多光谱影像，采用支持向量机（ SVM）法对森林植被进行分类研究，探讨了SVM法的分类能力以及纹理信息在森林植被分类中的影响。结果表明：Gram-Schmidt光谱锐化法是北京山区SPOT-5影像最佳的融合方法；SVM法在高分辨率影像森林植被分类中精度较高，不同核函数对分类精度的影响不显著；基于灰度共生矩阵产生的纹理信息能够提高SVM法的分类精度，3×3窗口是提高分类精度的最佳纹理窗口。
운용SPOT-5전색화다광보영상，채용지지향량궤（ SVM）법대삼림식피진행분류연구，탐토료SVM법적분류능력이급문리신식재삼림식피분류중적영향。결과표명：Gram-Schmidt광보예화법시북경산구SPOT-5영상최가적융합방법；SVM법재고분변솔영상삼림식피분류중정도교고，불동핵함수대분류정도적영향불현저；기우회도공생구진산생적문리신식능구제고SVM법적분류정도，3×3창구시제고분류정도적최가문리창구。
The experiment was conducted to classify the forest vegetation with support vector machine (SVM) method based on SPOT-5 panchromatic and multispectral images and explore the ability with SVM method and the effect by texture informa-tion in forest vegetation classification .Gram-Schmidt spectral sharpening method is the best fusion method forSPOT -5 im-age in Beijing mountain areas.SVM method has higher classification accuracy with the fine resolution images in the forest vegetation extraction.There is no significant difference on classification accuracy with different kernel functions.Image texture information from Gray level co-occurrence matrix ( GLCM) method can improve the classification accuracy by SVM method, and the best texturewindow of 3×3 windows can improve the classification accuracy obviously.