A New Hyperspectral Compressed Sensing Method for Efficient Satellite Communications
ID:169 View Protection:ATTENDEE Updated Time:2020-08-05 10:17:28 Hits:492 Oral Presentation

Start Time:2020-06-08 14:40(Asia/Shanghai)


Session:S Special Session » SS13Unsupervised Computing And Large-Scale Optimization For Multi-Dimensional Data Processing

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Directly transmitting the huge amount of typical hyperspectral data acquired on satellite to the ground station is inefficient. This paper proposes a new compressed sensing strategy for hyperspectral imagery on spaceborne sensors systems. As the onboard computing/storage resources are limited, e.g., on CubeSat, the measurement strategy should be computationally very light. Furthermore, considering the limited communication bandwidth, a very low sampling rate is desired. Our encoder accounts for these requirements by separately recording the spatial details and the spectral information, both of which essentially require only simple averaging operators. Our measurement strategy naturally induces a reconstruction criterion that can be elegantly interpreted as a well-known fusion problem in satellite remote sensing, allowing the adoption of a convex optimization method for simple and fast decoding. Our method, termed spatial/spectral compressed encoder (SPACE), is experimentally evaluated on real hyperspectral data, showing superior efficacy in terms of both sampling rate and reconstruction accuracy.
compressed sensing; hyperspectral imagery; spaceborne sensors systems; measurement strategy
Chia-Hsiang Lin
National Cheng Kung University, Taiwan

Submission Author
Chia-Hsiang Lin National Cheng Kung University, Taiwan
Jose Bioucas Instituto de Telecomunicoes, Portugal
Tzu-Hsuan Lin National Cheng Kung University, Taiwan
Yen-Cheng Lin National Cheng Kung University, Taiwan
Chi-Hung Kao National Cheng Kung University, Taiwan
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  • Jan 12 2020

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