传统的稀疏表示分类方法仅利用图像数据的稀疏特性分类, 未利用高光谱图像的邻域信息, 为此提出了一种联合稀疏特性和邻域相似度量的分类方法.该方法首先利用稀疏表示揭示出数据的稀疏特性, 然后计算在各类样本中的稀疏相似性, 并结合邻域特性, 构建数据在各类样本中的稀疏-邻域联合相似关系, 最后根据联合相似性大小判断数据类别.在利用数据的稀疏特性的同时结合像元的邻域信息, 增强各种地物类别间的区分性, 提升分类效果.在Indian Pines和PaviaU高光谱数据集上的实验表明：本文算法的分类精度高于其他方法, 总体分类精度分别达到了81.69%和86.59%, 能得到具有更多同质区域的分类结果图, 拥有更好的总体分类精度、平均分类精度和Kappa系数.

The traditional sparse representation classification methods only exploit the sparse property while they ignore the neighborhood similarity information in hyperspectral image. To address this problem, a novel sparsity-neighborhood metric classification method was proposed in this paper. Firstly, the proposed algorithm utilizes sparse representation to reveal the sparse properties of data, the following sparse similarity can be calculated in each class of samples. Then, according to neighborhood information, the method constructs the sparsity-neighborhood similarity relationship in each class of samples. Finally, the land cover types can be obtained with the federated sparsity-neighborhood similarity. The proposed algorithm possesses sparse property and neighborhood information, which can enhance the discrimination among different land cover classes to improve the classification performance. The experiments were performed on the Indian Pines and PaviaU hyperspectral data sets. Experimental results demonstrate that the proposed algorithm has better classification accuracy than other algorithms, the overall classification accuracies reach 81.69% and 86.59%, respectively. The proposed algorithm can obtain more homogeneous regions and improve classification accuracy and Kappa coefficient.