Sparse representations for online-learning-based hyperspectral image compression

Sparse models provide data representations in the fewest possible number of nonzero elements. This inherent characteristic enables sparse models to be utilized for data compression purposes. Hyperspectral data is large in size. In this paper, a framework for sparsity-based hyperspectral image compression methods using online learning is proposed. There are various sparse optimization models. A comparative analysis of sparse representations in terms of their hyperspectral image compression performance is presented. For this purpose, online-learning-based hyperspectral image compression methods are proposed using four different sparse representations. Results indicate that, independent of the sparsity models, online-learning-based hyperspectral data compression schemes yield the best compression performances for data rates of 0.1 and 0.3 bits per sample, compared to other state-of-the-art hyperspectral data compression techniques, in terms of image quality measured as average peak signal-to-noise ratio.