Hyperspectral image compressed sensing model based on the  collaborative sparsity of the intra-frame and inter-band

Wang, Xianghai; Hai-ying, Song; Song, Chuanming; Tao, Jingzhe

doi:10.1360/n112014-00402

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…从空间域上看, HSI 在每一个波段的图像都是当前波段下地物的光谱反射率, 每一个像素在相邻列和行像元之间也存在着一定的相关性, 通常称之为空间维的局部相关性 [18,19] . 这种空间维的局部相关性理论上表现为相邻的像元在光谱维上具有相似的光谱曲线, 本文对由这些相似的光谱曲线所形成的光谱维结构相关性进行研究.…”

Section: Hsi 光谱维结构相关性研究unclassified

“…进一步, 通过 SBI (split Bregman iteration) 算法 [23] 可上述问题转换为下列 3 步迭代子问题: 为了衡量重构 HSI 的质量, 需要从空间域和光谱域两个角度对重构图像的质量进行评价, 本文采用峰值信噪比 (PSNR) 和结构特征相似度 (FSIM) [25] 来对重构 HSI 空间恢复效果进行度量, 同时采用光谱角 (SAM) 来度量光谱的保持效果; 进一步与近年来的一些典型算法: GPSR [26] 算法、改进的 GPSR 算法 [27] 、全变分 (TV) 算法 [28] 和 HiCoSM 算法 [18] 进行对比.…”

Section: 基于 S-scosm 的 Hsi 重构模型unclassified

“…其中 µ 和 ζ 为正值参数, 本文实验中分别选取其为 0.025 和 0.05. 式(15) 的计算过程可参见文献[18,24]. 实验与分析为了验证本文重构模型的有效性, 选取了 4 类 HSI 波段组进行验证和评估.…”

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Spectral dimensional correlation and sparse reconstruction model of hyperspectral images

Wang

Xie

et al. 2021

Sci. Sin.-Inf.

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高光谱 (hyperspectral, HS) 传感器在获取地物空间遥感图像的同时可通过不同电磁波段获取近似连续的地物光谱曲线, 大大提高了对地物的分类和识别能力, 因而自诞生以来便受到地质勘探、大气环境、环境检测、海洋遥感等多个领域的特别关注 [1, 2]. 然而高光谱图像 (hyperspectral image, HSI) 所具有的大数据量、强波段间相关性和多冗余信息等也给其传输、处理和存储等实际应用带来巨大挑战, 如何有效地对其信息进行组织、挖掘系数间的相关性并捕捉其内在特性对于提高 HSI 信息处理的质量便显得十分重要 [3]. 压缩感知 (compressive sensing, CS) 理论 [4∼7] 的出现为解决上述问题提

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Section: Hsi 光谱维结构相关性研究unclassified

Section: 基于 S-scosm 的 Hsi 重构模型unclassified

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Spectral dimensional correlation and sparse reconstruction model of hyperspectral images

Wang

Xie

et al. 2021

Sci. Sin.-Inf.

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Subspace Dynamic Combined Sparsity-Based Hyper-Sharpening for Diverse Auxiliary Images

Tao,

Feng,

Song

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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Hyperspectral Image Reconstruction Based on Spatial-Spectral Domains Low-Rank Sparse Representation

et al. 2022

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The enormous amount of data that are generated by hyperspectral remote sensing images (HSI) combined with the spatial channel’s limited and fragile bandwidth creates serious transmission, storage, and application challenges. HSI reconstruction based on compressed sensing has become a frontier area, and its effectiveness depends heavily on the exploitation and sparse representation of HSI information correlation. In this paper, we propose a low-rank sparse constrained HSI reconstruction model (LRCoSM) that is based on joint spatial-spectral HSI sparseness. In the spectral dimension, a spectral domain sparsity measure and the representation of the joint spectral dimensional plane are proposed for the first time. A Gaussian mixture model (GMM) that is based on unsupervised adaptive parameter learning of external datasets is used to cluster similar patches of joint spectral plane features, capturing the correlation of HSI spectral dimensional non-local structure image patches while performing low-rank decomposition of clustered similar patches to extract feature information, effectively improving the ability of low-rank approximate sparse representation of spectral dimensional similar patches. In the spatial dimension, local-nonlocal HSI similarity is explored to refine sparse prior constraints. Spectral and spatial dimension sparse constraints improve HSI reconstruction quality. Experimental results that are based on various sampling rates on four publicly available datasets show that the proposed algorithm can obtain high-quality reconstructed PSNR and FSIM values and effectively maintain the spectral curves for few-band datasets compared with six currently popular reconstruction algorithms, and the proposed algorithm has strong robustness and generalization ability at different sampling rates and on other datasets.

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Hyperspectral image compressed sensing model based on the collaborative sparsity of the intra-frame and inter-band

Cited by 3 publications

References 3 publications

Spectral dimensional correlation and sparse reconstruction model of hyperspectral images

Spectral dimensional correlation and sparse reconstruction model of hyperspectral images

Subspace Dynamic Combined Sparsity-Based Hyper-Sharpening for Diverse Auxiliary Images

Hyperspectral Image Reconstruction Based on Spatial-Spectral Domains Low-Rank Sparse Representation

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