Learning-Based Hyperspectral Imagery Compression through Generative Neural Networks

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 21; p. 3657
• Main Authors: Deng, Chubo, Cen, Yi, Zhang, Lifu
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2020
• Subjects: Algorithms; Brain; Compression; Compression ratio; compression sensing; Deep learning; Embedding; Experiments; feature reduction; generative neural network; hyperspectral; Hyperspectral imaging; Image compression; Image reconstruction; Machine learning; Neural networks; Principal components analysis; Probability distribution; Random variables; Spectral bands; Wavelengths; Wavelet transforms
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12213657

Hyperspectral images (HSIs), which obtain abundant spectral information for narrow spectral bands (no wider than 10 nm), have greatly improved our ability to qualitatively and quantitatively sense the Earth. Since HSIs are collected by high-resolution instruments over a very large number of wavelengths, the data generated by such sensors is enormous, and the amount of data continues to grow, HSI compression technique will play more crucial role in this trend. The classical method for HSI compression is through compression and reconstruction methods such as three-dimensional wavelet-based techniques or the principle component analysis (PCA) transform. In this paper, we provide an alternative approach for HSI compression via a generative neural network (GNN), which learns the probability distribution of the real data from a random latent code. This is achieved by defining a family of densities and finding the one minimizing the distance between this family and the real data distribution. Then, the well-trained neural network is a representation of the HSI, and the compression ratio is determined by the complexity of the GNN. Moreover, the latent code can be encrypted by embedding a digit with a random distribution, which makes the code confidential. Experimental examples are presented to demonstrate the potential of the GNN to solve image compression problems in the field of HSI. Compared with other algorithms, it has better performance at high compression ratio, and there is still much room left for improvements along with the fast development of deep-learning techniques.