Abstract
The ever-increasing resolution puts tremendous pressure to the onboard hyperspectral imaging system. Compressed sensing technology is one of the important ways to solve this problem. Distributed compressed sensing was proposed to exploit both intra- and inter-correlation structures of hyperspectral images. However, the implementation method of distributed compressed sampling has not been reported, and the joint sparsity reconstruction algorithm cannot achieve excellent image reconstruction performance. In this paper, a distributed compressed sampling strategy inspired by distributed compressed video sensing and optical implementation model are proposed to collect compressed hyperspectral data. In the image reconstruction process, we discard the direct application of the joint sparsity constraint on the data itself. Instead, we explore the estimation method of abundance and endmember with the help of the existing spectral library. Then, the images are recovered by applying the linear mixing model of hyperspectral. The comparison experiments of various schemes show that the proposed compressed sensing scheme has an obvious advantage in reconstruction performance under the low sampling rate. The proposed compressed sensing scheme has great potential in a high-compression ratio onboard hyperspectral imaging system.
Highlights
Hyperspectral images (HSI) are widely used in scene classification [1], environmental monitoring [2], mineral detection and exploration [3], [4], and military surveillance [5] since their rich details of material substances
Spatial-spectral compressed reconstruction based on spectral unmixing (SSCR_SU) [12] divided hyperspectral images into two parts: spatial compressed sampling and spectral compressed sampling to estimate endmembers and abundances
Our DCS_SLM and Spectral compressed acquisition (SpeCA) methods are based on linear mixing model (LMM), and other approaches to restore the HSI rely on the global sparse prior
Summary
Hyperspectral images (HSI) are widely used in scene classification [1], environmental monitoring [2], mineral detection and exploration [3], [4], and military surveillance [5] since their rich details of material substances. The early reconstruction algorithms mainly focus on the sparse, smooth and low rank priors of hyperspectral data matrix. Previous works on hyperspectral compressed sensing reconstruction using spectral unmixing has made great progress. Spatial-spectral compressed reconstruction based on spectral unmixing (SSCR_SU) [12] divided hyperspectral images into two parts: spatial compressed sampling and spectral compressed sampling to estimate endmembers and abundances. A distributed compressed sampling and reconstruction strategy is proposed for hyperspectral CS imaging. We call the proposed procedure distributed compressed sensing according to spectral library matching or DCS_SLM. A distributed compressed imaging framework and optical implementation model without additional computational burden are proposed. To effectively recover hyperspectral images from distributed compressed data, we propose a LMM-based reconstruction scheme.
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