Open AccessHyperspectral Acquisition Technology Based on Compressed Sampling in Spatial Domain
Author(s) -
Shasha Tian,
Zhen Zhao,
Tao Hou,
Liancheng Zhang
Publication year - 2022
Publication title -
international journal of circuits, systems and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.156
H-Index - 13
ISSN - 1998-4464
DOI - 10.46300/9106.2022.16.39
Subject(s) - hyperspectral imaging , compressed sensing , computer science , computer vision , artificial intelligence , redundancy (engineering) , full spectral imaging , data compression , sampling (signal processing) , iterative reconstruction , algorithm , pattern recognition (psychology) , filter (signal processing) , operating system
In the hyperspectral imaging device, the sensor detects the reflection or radiation intensity of the target at hundreds of different wavelengths, thus forming a spectral image composed of hundreds of continuous bands. The traditional processing method of sampling first and then compressing not only cannot fundamentally solve the problem of huge amount of data, but also causes waste of resources. To solve this problem, a spectral image reconstruction method based on compressed sampling in spatial domain and transform coding in spectral domain is designed by using the sparsity of single-band two-dimensional image and the spectral redundancy of spatial coded data. Based on Bayesian theory, a compressed sensing measurement matrix of adaptive projection is proposed. Combining these two algorithms, an adaptive Grouplet-FBCS algorithm is constructed to reconstruct the image using smooth projection Landweber. Experimental results show that, compared with existing image block compression sensing algorithms, this algorithm can significantly improve the quality of image signal reconstruction.