Adaptive Spectral–Spatial Compression of Hyperspectral Image With Sparse Representation

Sparse representation (SR) can transform spectral signatures of hyperspectral pixels into sparse coefficients with very few nonzero entries, which can efficiently be used for compression. In this paper, a spectral-spatial adaptive SR (SSASR) method is proposed for hyperspectral image (HSI) compression by taking advantage of the spectral and spatial information of HSIs. First, we construct superpixels, i.e., homogeneous regions with adaptive sizes and shapes, to describe HSIs. Since homogeneous regions usually consist of similar pixels, pixels within each superpixel will be similar and share similar spectral signatures. Then, the spectral signatures of each superpixel can be simultaneously coded in the SR model to exploit their joint sparsity. Since different superpixels generally have different performances of SR, their rate-distortion performances in the sparse coding will be different. To achieve the best possible overall rate-distortion performance, an adaptive coding scheme is introduced to adaptively assign distortions to superpixels. Finally, the obtained sparse coefficients are quantized and entropy coded and constitute the final bitstream with the coded superpixel map. The experimental results over several HSIs show that the proposed SSASR method outperforms some state-of-the-art HSI compression methods in terms of the rate-distortion and spectral fidelity performances.