Hyperspectral Image Compression and Super-Resolution Using Tensor Decomposition Learning

As the field of remote sensing for Earth Observation is rapidly evolving, there is an increasing demand for developing suitable methods to store and transmit the massive amounts of the generated data. At the same time, as multiple sensors acquire observations with different dimensions, super-resolution methods come into play to unify the framework for upcoming statistical inference tasks. In this paper, we employ a tensor-based structuring of multi-spectral image data and we propose a low-rank tensor completion scheme for efficient image-content compression and recovery. To address the problem of low-resolution imagery, we further provide a robust algorithmic scheme for super-resolving satellite images, followed by a state-of-the-art convolutional neural network architecture serving the classification task of the employed images. Experimental analysis on real-world observations demonstrates the detrimental effects of image compression on classification, an issued successfully addressed by the proposed recovery and super-resolution schemes.