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Three-Stages Hyperspectral Image Compression Sensing with Band Selection

Jingbo Zhang, Yanjun Zhang, Xingjuan Cai*, Liping Xie*

Taiyuan University of Science and Technology, Taiyuan, 030024, China

* Corresponding Authors: Xingjuan Cai. Email: email; Liping Xie. Email: email

(This article belongs to this Special Issue: Swarm Intelligence and Applications in Combinatorial Optimization)

Computer Modeling in Engineering & Sciences 2023, 134(1), 293-316.


Compressed sensing (CS), as an efficient data transmission method, has achieved great success in the field of data transmission such as image, video and text. It can robustly recover signals from fewer Measurements, effectively alleviating the bandwidth pressure during data transmission. However, CS has many shortcomings in the transmission of hyperspectral image (HSI) data. This work aims to consider the application of CS in the transmission of hyperspectral image (HSI) data, and provides a feasible research scheme for CS of HSI data. HSI has rich spectral information and spatial information in bands, which can reflect the physical properties of the target. Most of the hyperspectral image compressed sensing (HSICS) algorithms cannot effectively use the inter-band information of HSI, resulting in poor reconstruction effects. In this paper, A three-stage hyperspectral image compression sensing algorithm (Three-stages HSICS) is proposed to obtain intra-band and inter-band characteristics of HSI, which can improve the reconstruction accuracy of HSI. Here, we establish a multi-objective band selection (Mop-BS) model, a multi-hypothesis prediction (MHP) model and a residual sparse (ReWSR) model for HSI, and use a staged reconstruction method to restore the compressed HSI. The simulation results show that the three-stage HSICS successfully improves the reconstruction accuracy of HSICS, and it performs best among all comparison algorithms.


Cite This Article

Zhang, J., Zhang, Y., Cai, X., Xie, L. (2023). Three-Stages Hyperspectral Image Compression Sensing with Band Selection. CMES-Computer Modeling in Engineering & Sciences, 134(1), 293–316.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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