遥感图像森林类型小波纹理的SVM法分类

利用遥感图像对森林类型进行分类是大面积地调查、监测、分析森林资源的快速与经济的方法，但由于不同森林的光谱特征非常相近而较难准确分类。因此，在GPS数据和高分辨率遥感图像的支持下，对水源林Landsat TM遥感图像用窗口法获得阔叶林、针叶林和竹林样本图像，然后计算其小波分解后小波系数的l1范数纹理测度构成分类特征向量，利用支持向量基SVM进行分类。结果表明，利用SVM对图像中阔叶林、针叶林和竹林分类平均精度在80%以上，可较准确地识别森林类型，图像总体分类精度达到90.2%，Kappa系数0.77，均比利用小波纹理特征的神经网络法和最大似然法有所提高，森林分类错误产生的主要原因是混交林造成两类森林间存在交集。该方法可以较有效地提高遥感图像森林类型的分类精度。

Forest species classification in remote sensing image with wavelet texture and SVM

Remote sensing forest classification is fast and economical method to survey,inspect and analyze vast forest resource.However,it is very difficult to classify forest species accurately because of their similar spectrums.Therefore,with the assistance of high spatial resolution images and GIS,broadleaf,conifer and bamboo images are segmented with window on Landsat TM image of riverhead forest.Wavelet coefficient l1 norm of texture features are got as classification feature vectors after the sample images are decomposed with wavelet transform.Finally,TM image is classified with Support Vector Machine(SVM).Results show that the average classification accuracies for broadleaf,conifer and bamboo in TM image are over 80%.Forest species can be distinguished correctly with SVM.Total classification accuracy for the whole image is 90.2% and Kappa coefficient is 0.77,which are higher than those with neural network and maximum likelihood algorithms.Errors mainly result from mixed forests and intersection between two forest types.The forest species classification accuracy in remote sensing images can be enhanced with the algorithm effectively.