Reconstruction of hyperspectral images with compressed sensing based on linear mixing model and affinity propagation clustering algorithm

Youli zou, Zhi-yun Xiao, Kuntao Ye
{"title":"Reconstruction of hyperspectral images with compressed sensing based on linear mixing model and affinity propagation clustering algorithm","authors":"Youli zou, Zhi-yun Xiao, Kuntao Ye","doi":"10.1145/3599589.3599602","DOIUrl":null,"url":null,"abstract":"The increasing spatial and spectral resolution of hyperspectral images results in a significant rise in data volume, which poses a challenge for data storage and transmission. Therefore, improving the efficiency of storage and transmission by enhancing the reconstruction performance of hyperspectral images at low sampling rates or same sampling rates conditions is a crucial topic in compressed sensing. Previous research has shown that a linear mixing model and distributed compressed sensing method outperform traditional compressed sensing reconstruction algorithms in recovering original data. However, the low estimating accuracy of both the endmembers matrix and abundance matrix due to the random selection of reference bands limits the reconstruction performance. To address this problem, we proposed a compressed sensing reconstruction algorithm based on a linear mixing model and affinity propagation clustering algorithm. Our method improves reconstruction performance by enhancing the estimating accuracy of the endmembers and abundance matrices. During the sampling stage, the affinity propagation clustering algorithm is used to group the spectral bands according to the spectral correlation of hyperspectral images, where the clustering center serving as the reference band and the other bands as non-reference bands. During the reconstruction stage, the number of endmembers from the reference band is estimated fist, and the endmembers matrix and the abundance matrix are then estimated. Finally, the endmembers matrix and estimated abundance matrix are used for reconstruction. Experimental results show that our proposed algorithm achieves higher performance in reconstructing hyperspectral images than the linear mixing model-based distributed compressed sensing method.","PeriodicalId":123753,"journal":{"name":"Proceedings of the 2023 8th International Conference on Multimedia and Image Processing","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2023 8th International Conference on Multimedia and Image Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3599589.3599602","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The increasing spatial and spectral resolution of hyperspectral images results in a significant rise in data volume, which poses a challenge for data storage and transmission. Therefore, improving the efficiency of storage and transmission by enhancing the reconstruction performance of hyperspectral images at low sampling rates or same sampling rates conditions is a crucial topic in compressed sensing. Previous research has shown that a linear mixing model and distributed compressed sensing method outperform traditional compressed sensing reconstruction algorithms in recovering original data. However, the low estimating accuracy of both the endmembers matrix and abundance matrix due to the random selection of reference bands limits the reconstruction performance. To address this problem, we proposed a compressed sensing reconstruction algorithm based on a linear mixing model and affinity propagation clustering algorithm. Our method improves reconstruction performance by enhancing the estimating accuracy of the endmembers and abundance matrices. During the sampling stage, the affinity propagation clustering algorithm is used to group the spectral bands according to the spectral correlation of hyperspectral images, where the clustering center serving as the reference band and the other bands as non-reference bands. During the reconstruction stage, the number of endmembers from the reference band is estimated fist, and the endmembers matrix and the abundance matrix are then estimated. Finally, the endmembers matrix and estimated abundance matrix are used for reconstruction. Experimental results show that our proposed algorithm achieves higher performance in reconstructing hyperspectral images than the linear mixing model-based distributed compressed sensing method.
基于线性混合模型和亲和传播聚类算法的压缩感知高光谱图像重建
随着高光谱图像空间分辨率和光谱分辨率的提高,数据量大幅增加,这对数据存储和传输提出了挑战。因此,通过提高低采样率或相同采样率条件下高光谱图像的重建性能来提高存储和传输效率是压缩感知中的一个重要课题。已有研究表明,线性混合模型和分布式压缩感知方法在恢复原始数据方面优于传统压缩感知重建算法。然而,由于参考波段的随机选择,端元矩阵和丰度矩阵的估计精度较低,限制了重构的性能。为了解决这一问题,我们提出了一种基于线性混合模型和亲和传播聚类算法的压缩感知重构算法。该方法通过提高端元和丰度矩阵的估计精度来提高重建性能。在采样阶段,采用亲和传播聚类算法,根据高光谱图像的光谱相关性对光谱波段进行分组,其中聚类中心作为参考波段,其他波段作为非参考波段。在重建阶段,首先估计参考带的端元个数,然后估计端元矩阵和丰度矩阵。最后,利用端元矩阵和估计丰度矩阵进行重构。实验结果表明,与基于线性混合模型的分布式压缩感知方法相比,本文算法在高光谱图像重建方面取得了更高的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信