@Article{cmes.2022.020426,
AUTHOR = {Jingbo Zhang, Yanjun Zhang, Xingjuan Cai, Liping Xie},
TITLE = {Three-Stages Hyperspectral Image Compression Sensing with Band Selection},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {134},
YEAR = {2023},
NUMBER = {1},
PAGES = {293--316},
URL = {http://www.techscience.com/CMES/v134n1/49439},
ISSN = {1526-1506},
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.},
DOI = {10.32604/cmes.2022.020426}
}