Large-scale hyperspectral image compression via sparse representations based on online learning

Abstract

In this study, proximity based optimization algorithms are used for lossy compression of hyperspectral images that are inherently large scale. This is the first time that such proximity based optimization algorithms are implemented with an online dictionary learning method. Compression performances are compared with the one obtained by various sparse representation algorithms. As a result, proximity based optimization algorithms are listed among the three best ones in terms of compression performance values for all hyperspectral images. Additionally, the applicability of anomaly detection is tested on the reconstructed images.

Comprehensive review of hyperspectral image compression algorithms

“…Can reconstruct the original real values of pixels. 75 GIST, SpaRSA, 76 spectral-spatial adaptive sparse representation (SSASR), 77 and TwIST. 76 Technique.…”

“…75 GIST, SpaRSA, 76 spectral-spatial adaptive sparse representation (SSASR), 77 and TwIST. 76 Technique. Sparse coding is used by multiple algorithms in different style,and a generalized and in-depth description of the technique is given in Fig.…”

Lossy Compressive Sensing Based on Online Dictionary Learning

“…Using dictionary learning in the lossy hyperspectral image compression algorithms is quite common [18,38,40]. In the literature [36,37], it is shown that online dictionary learning algorithm is more effective in processing large datasets with sequentially arriving samples such as hyperspectral images. This sparse representation process finds the sparsest solution, which means solving the non-deterministic polynomial-time hard (NP-hard) 0 -norm minimization problem [10].…”