Open Access

ARTICLE

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: ; Liping Xie. Email:

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

Computer Modeling in Engineering & Sciences 2023, 134(1), 293-316. https://doi.org/10.32604/cmes.2022.020426

Received 23 December 2021; Accepted 28 February 2022; Issue published 24 August 2022

Abstract

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.

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Keywords

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