Classification of Hyperspectral Image Based on Sparse Representation in Tangent Space

In many real-world problems, data always lie in a low-dimensional manifold. Exploiting the manifold can greatly enhance the discrimination between different categories. In this letter, we propose a classification framework based on sparse representation to directly exploit the underlying manifold. Specifically, using the tangent plane to approximate the local manifold of each test sample, the proposed method classifies the sample by sparse representation in tangent space. Unlike several existing sparse-representation-based classification methods, which sparsely represent the test sample itself, the proposed method sparsely represents the local manifold of the test sample by tangent plane approximation. Therefore, it goes beyond the sample itself and is more robust to kinds of variations confronted in hyperspectral image (HSI) such as illustration differences and spectrum mixing. Experimental results show that the proposed algorithm outperforms several state-of-the-art methods for the classification of HSI with limited training samples.