Manifold-Based Sparse Representation for Hyperspectral Image Classification

Abstract

A sparsity-based model has led to interesting results in hyperspectral image (HSI) classification. Sparse representation from a test sample is used to identify the class label. However, an $\ell_{1}$ -based sparse algorithm sometimes yields unstable sparse representation. Inspired by recent progress in manifold learning, two manifold-based sparse representation algorithms are proposed to exploit the local structure of the test samples in corresponding sparse representations for enforcing smoothness across neighboring samples' sparse representations. Using techniques from regularization and local invariance, two manifold-based regularization terms are incorporated into the $\ell_{1}$ -based objective function. Extensive experiments show that our proposed algorithms obtain excellent classification performance on three classic HSIs.