Sparse and Smooth Feature Extraction for Hyperspectral Imagery

Abstract

In this paper, a hyperspectral feature extraction (FE) method called sparse and smooth low-rank analysis (SSLRA) is proposed. First, we propose a new low-rank model for hyperspectral images (HSIs). In the new model, HSI is decomposed into smooth and sparse unknown features which live in an unknown orthogonal subspace. Then, the sparse and smooth features are simultaneously estimated using a non-convex constrained penalized cost function. In the experiments' SSLRA is applied on a real HSI and the smooth features extracted are used for the HSI classification. The results confirm improvements in classification accuracies compared to state-of-the-art FE methods.