Collaborative Sparse Coding with Smoothness Regularization for Hyperspectral-Image Classification

Abstract

Hyperspectral-image (HSI) classification plays a key role in numerous applications in remote sensing, urban planning, and environmental monitoring. Sparse-representation models have been increasingly explored for HSI classification because of their compactness, flexibility, and discriminative power. In this article, a novel HSI scheme is proposed based on collaborative sparse coding combined with smoothness regularization (CSCSR). First, based on the spatial piecewise continuity of HSIs, spectral-spatial preprocessing is performed to improve classification accuracy. Second, a collaborative sparse-coding model with smoothness regularization is proposed and applied for HSI classification. In our model, the sparsity level and smoothness regularization are tuned to improve classification performance. In addition, weighted pixel similarities are computed in pixel neighborhoods and then used to incorporate spatial information in the HSI classification scheme. A feature-sign search algorithm is used for sparse coding of feature descriptors. Experimental results on real HSI data sets demonstrate that the proposed CSCSR method effectively outperforms the current state-of-the-art HSI classifiers in terms of both qualitative and quantitative metrics.