多光谱遥感图像的波段设定在理论和实践上都极具地物针对性，在更有效的数据挖掘方法帮助下可以提取足够的光谱特征以区分地物，本文将光谱匹配技术用于相似性度量，以提高分类准确度.首先选定光谱角制图、光谱相关制图、Mahalanobis距离、光谱相似度和光谱信息差异做为光谱度量;随后选择高斯核函数，在选定核函数之后，得到了核映射下的光谱特征度量来挖掘高光谱遥感数据的光谱特征.最后采用核映射多光谱特征融合法对多光谱遥感图像光谱特征的相似性进行描述，得到基于核映射多光谱特征融合的高光谱遥感图像分类算法.使用MATLAB中的LIBSVM工具箱对AVIRIS高光谱遥感数据进行分类实验，并与已有算法进行对比，结果表明本文提出的算法具有较高的分类准确度和性能.

The spectral bands have strong relation with land covers both partically and theoretically.Thus it is possible to extract enough spectral features with the help of more efficient data represent methods to distinguish land covers. More pertinent spectral matching methods can be taken to improve the similarity and dissimilarity metric and to improve the performance of the classifiers.A couple of classic and efficient spectral measures,such as spectral angle mapper,spectral correlation mapper,mahalanobis distance,spectral similarity value and spectral information divergence,were selected.Then the RBF Guassian function was used and the spectral measure under the kernel mapping were obtained.A new method based on the fusion of multifeatures under kernel mapping was taken to dig the features of hyperspectral remote sensing data.Profile the similarity between different classes and thus a new classification algorithm was proposed.At last,this method was applied to a hyperspectral remotely sensing AVIRIS dataset named 92AV3C using the LIBSVM toolbox of MATLAB.The results show that the classification method of hyperspectral images by fusion of multifeatures under kernel mapping can significantly improve the accuracy of the classification. Experimental comparison shows the proposed algorithm can provide better performance for the pixel classification of hyperspectral image than many other wellknown techniques.