Hyperspectral image and videos provide rich spectral information content, which facilitates accurate classification, unmixing, temporal change detection, and so on. However, with the rapid improvements in technology, the data size has increased many folds. To properly handle the enormous data volume, efficient methods are required to compress the data. This paper proposes a multi-way approach for compression of the hyperspectral image or video sequence. In this approach, a differential representation of the data is first obtained. In the case of hyperspectral images, the difference between consecutive bands is obtained and in case of videos, the difference between consecutive frames is computed. In the next step, a sparse Tucker tensor decomposition is performed and the sparse core tensor obtained. Finally, the core tensor and the corresponding factor matrices are truncated and the data encoded to obtain the compressed version for transmission. The compression method utilises the multi-way structure of the data and hence can be extended for hyperspectral videos. Experimental results on several real data imply that the proposed compression approach obtains better efficiency in terms of compression ratio, signal to noise ratio. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Dictionary pruning step is often employed prior to the sparse unmixing process to improve the performance of library aided unmixing. This paper presents a novel recursive PCA approach for dictionary pruning of linearly mixed hyperspectral data motivated by the low-rank structure of a linearly mixed hyperspectral image. Further, we propose a mutual coherence reduction method for pre-unmixing to enhance the performance of pruning. In the pruning step we, identify the actual image endmembers utilizing the low-rank constraint. We obtain an augmented version of the data by appending each image endmember and compute PCA reconstruction error, which is a convex surrogate of matrix rank. We identify the pruned library elements according to PCA reconstruction error ratio (PRER) and PCA reconstruction error difference (PRED) and employ a recursive formulation for repeated PCA computation. Our proposed formulation identifies the exact endmember set at an affordable computational requirement. Extensive simulated and real image experiments exhibit the efficacy of the proposed algorithm in terms of its accuracy, computational complexity and noise performance.

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