针对传统高光谱影像地物分类算法大多仅考虑光谱信息而忽略空间邻近像元间相关性的问题，提出了一种空-谱协同嵌入（SSCE）降维算法和空-谱协同最近邻（SSCNN）分类器。首先，定义一种空-谱协同距离，并将其应用于近邻选取和低维嵌入；然后，构建空-谱近邻关系图来保持数据中的流形结构，并在权值设置中增大空间近邻点的权重以增强数据间的聚集性，提取鉴别特征；最后使用SSCNN分类器对降维后的数据进行分类。利用PaviaU和Salinas高光谱数据集进行试验验证，结果表明，与传统的光谱分类算法相比，该算法能有效提高高光谱影像的地物分类精度。
Aiming at the problem that in hyperspectral image land cover classification, the traditional classification methods just apply the spectral information while they ignore the relationship between the spatial neighbors, a new dimensionality algorithm called spatial-spectral coordination embedding (SSCE) and a new classifier called spatial-spectral coordination nearest neighbor (SSCNN) were proposed in this paper. Firstly, the proposed method defines a spatial-spectral coordination distance and the distance is applied to the neighbor selection and low-dimensional embedding. Then, it constructs a spatial-spectral neighborhood graph to maintain the manifold structure of the data set, and enhances the aggregation of data through raising weight of the spatial neighbor points to extract the discriminant features. Finally, it uses the SSCNN to classify the reduced dimensional data. Experimental results using PaviaU and Salinas data set show that the proposed method can effectively improve ground objects classification accuracy comparing with traditional spectral classification methods.
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