Hyperspectral Image Classification via Weighted Joint Nearest Neighbor and Sparse Representation

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The $k$ -nearest neighbor ( $k$ -NN) method relies on Euclidean distance as a classification measure to obtain the labels of the test samples. Recently, many studies show that joint region of test samples can make full use of the spatial information of hyperspectral image. However, traditional joint $k$ -NN algorithm holds that the weight of the each test sample in a local region is identical, which is not reasonable, since each test sample may have different importance and distribution. To solve this problem, a weighted joint nearest neighbor and sparse representation method is proposed in this paper, which consists of the following steps: first, a Gaussian weighted function has been introduced into the joint region of test pixels so as to obtain the weighted joint Euclidean distance. Next, the sparse representation-based method is adopted to obtain the representation residuals. Finally, a decision function is applied to achieve the balance between the weighted joint Euclidean distance and residual of the sparse representation. Experiments performed on the four real HSI datasets have demonstrated that the proposed methods can achieve better performance than several previous methods.