Manifold-Based Sparse Representation for Hyperspectral Image Classification

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 ℓ₁-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 ℓ₁-based objective function. Extensive experiments show that our proposed algorithms obtain excellent classification performance on three classic HSIs.