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A survey on hyperspectral image restoration: from the view of low-rank tensor approximation

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Abstract

The ability to capture fine spectral discriminative information enables hyperspectral images (HSIs) to observe, detect and identify objects with subtle spectral discrepancy. However, the captured HSIs may not represent the true distribution of ground objects and the received reflectance at imaging instruments may be degraded, owing to environmental disturbances, atmospheric effects, and sensors’ hardware limitations. These degradations include but are not limited to complex noise, heavy stripes, deadlines, cloud/shadow occlusion, blurring and spatial-resolution degradation, etc. These degradations dramatically reduce the quality and usefulness of HSIs. Low-rank tensor approximation (LRTA) is such an emerging technique, having gained much attention in the HSI restoration community, with an ever-growing theoretical foundation and pivotal technological innovation. Compared to low-rank matrix approximation (LRMA), LRTA characterizes more complex intrinsic structures of high-order data and owns more efficient learning abilities, being established to address convex and non-convex inverse optimization problems induced by HSI restoration. This survey mainly attempts to present a sophisticated, cutting-edge, and comprehensive technical survey of LRTA toward HSI restoration, specifically focusing on the following six topics: denoising, fusion, destriping, inpainting, deblurring, and super-resolution. For each topic, state-of-the-art restoration methods are introduced, with quantitative and visual performance assessments. Open issues and challenges are also presented, including model formulation, algorithm design, prior exploration, and application concerning the interpretation requirements.

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Acknowledgements

This work was supported by National Key R&D Program of China (Grant No. 2021YFB3900502), China Postdoctoral Science Foundation (Grant No. 2021M700440), National Natural Science Foundation of China (Grant No. 61922013), and Beijing Natural Science Foundation (Grant No. L191004).

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Authors and Affiliations

  1. School of Information and Electronics and Beijing Key Laboratory of Fractional Signals and Systems, Beijing Institute of Technology, Beijing, 100081, China

    Na Liu, Wei Li, Yinjian Wang & Ran Tao

  2. Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS, 39762, USA

    Qian Du

  3. GIPSA-Lab, University of Grenoble Alpes, Grenoble, 38000, France

    Jocelyn Chanussot

  4. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China

    Jocelyn Chanussot

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  1. Na Liu
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  2. Wei Li
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Correspondence to Wei Li.

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Liu, N., Li, W., Wang, Y. et al. A survey on hyperspectral image restoration: from the view of low-rank tensor approximation. Sci. China Inf. Sci. 66, 140302 (2023). https://doi.org/10.1007/s11432-022-3609-4

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