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摘要： 为有效解决高分辨率多光谱遥感影像分类模糊性和不确定性以及较好地克服噪声的影响，提出了一种基于双树复小波分解的BP神经网络遥感图像分类方法。首先提取影像的NDVI、纹理特征来降低影像中因“同谱异物”和“同物异谱”引起的分类不确定性；然后对影像的原始光谱波段、NDVI、纹理特征图像进行一层双树复小波分解，提取出图像的低频信息，降低图像噪声以及减少分类中存在的“椒盐”现象；最后将提取的低频子图作为BP神经网络的输入并根据训练好的网络进行分类，得到最终的分类结果。对比实验结果表明该方法的分类结果杂点较少，区域一致性更强，具有较高的分类精度和较好的鲁棒性。 关键词： 归一化植被指数;  纹理;  灰度共生矩阵;  双树复小波变换;  BP神经网络     Abstract: In order to solve the ambiguity and uncertainty of high resolution multi\|spectral remote sensing image classification and to better overcome the influence of noise，a new BPNN（Back Propagation Neural Network）classification method of multi\|spectral image，based on DT\|CWT decomposition，is presented in this paper.First，the NDVI and texture features of the image are extracted to reduce the classification uncertainty caused by the problem of different objects having the same spectrum and the same objects having different spectrum in the image，then，the original spectral band，NDVI and texture features of the image are decomposed by DT\|CWT to extract the Low\|frequency information of the image，as well as to reduce the image noise and the presence of “salt and pepper” in the classification.Finally，the extracted low\|frequency sub\|graphs are input to the BP neural network and classified according to the trained network to obtain the final classification result.The results of the comparison show that the proposed method with less miscellaneous points has stronger regional consistency，higher classification accuracy and better robustness.

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