为了进一步提高稀疏表示分类能力，提出了基于联合稀疏表示算法与形态学特征的高光谱图像（HSI）分类算法。该算法对高光谱图像提取主成分特征图，并利用结构元素对主成分特征图进行多维的空间结构特征提取，结合提取的形态学特征与原始光谱特征，利用联合稀疏表示算法将同一空间区域中的像元联合进行稀疏系数矩阵的求解，最终通过最小残差判断准则确定像元类别。在AVIRIS与ROSIS HSI上的实验结果表明，该算法在分类效果和分类总精度上都有显著提高。

In order to further improve the classification performance of sparse representation classification, a hyperspectral image (HSI) classification algorithm based on joint sparse representation with morphological feature extraction is proposed. To obtain the principle component images, the whole HSI is analyzed by principle component analysis. The closing and opening operations are implemented on principle component images to extract the morphological features. Combining the original spectral and the morphological feature, the pixels in a local region around the central test pixel are simultaneously represented by a set of common atoms of new training dictionary. The classification of HSI is determined by computing the minimum reconstruction error between testing samples and training samples. Experimental results on AVIRIS and ROSIS HSI demonstrate that the effectiveness of the proposed method for improving the classification accuracy and performance.