A new hyperspectral compressed sensing method for efficient satellite communications

Chia Hsiang Lin, José M. Bioucas Dias, Tzu Hsuan Lin, Yen Cheng Lin, Chi Hung Kao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop, SAM 2020
PublisherIEEE Computer Society
ISBN (Electronic)9781728119465
DOIs
Publication statusPublished - 2020 Jun
Event11th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2020 - Hangzhou, China
Duration: 2020 Jun 82020 Jun 11

Publication series

NameProceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop
Volume2020-June
ISSN (Electronic)2151-870X

Conference

Conference11th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2020
CountryChina
CityHangzhou
Period20-06-0820-06-11

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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