Fusion of satellite images using Compressive Sampling Matching Pursuit (CoSaMP) method

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI:10.1109/NCVPRIPG.2013.6776256

B. Sathyabama, S. Sankari, S. Nayagara

{"title":"Fusion of satellite images using Compressive Sampling Matching Pursuit (CoSaMP) method","authors":"B. Sathyabama, S. Sankari, S. Nayagara","doi":"10.1109/NCVPRIPG.2013.6776256","DOIUrl":null,"url":null,"abstract":"Fusion of Low Resolution Multi Spectral (LRMS) image and High Resolution Panchromatic (HRPAN) image is a very important topic in the field of remote sensing. This paper handles the fusion of satellite images with sparse representation of data. The High resolution MS image is produced from the sparse, reconstructed from HRPAN and LRMS images using Compressive Sampling Matching Pursuit (CoSaMP) based on Orthogonal Matching Pursuit (OMP) algorithm. Sparse coefficients are produced by correlating the LR MS image patches with the LR PAN dictionary. The HRMS is formed by convolving the Sparse coefficients with the HR PAN dictionary. The world view -2 satellite images (HRPAN and LRMS) of Madurai, Tamil Nadu are used to test the proposed method. The experimental results show that this method can well preserve spectral and spatial details of the input images by adaptive learning. While compared to other well-known methods the proposed method offers high quality results to the input images by providing 87.28% Quality with No Reference (QNR).","PeriodicalId":436402,"journal":{"name":"2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NCVPRIPG.2013.6776256","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 7

Abstract

Fusion of Low Resolution Multi Spectral (LRMS) image and High Resolution Panchromatic (HRPAN) image is a very important topic in the field of remote sensing. This paper handles the fusion of satellite images with sparse representation of data. The High resolution MS image is produced from the sparse, reconstructed from HRPAN and LRMS images using Compressive Sampling Matching Pursuit (CoSaMP) based on Orthogonal Matching Pursuit (OMP) algorithm. Sparse coefficients are produced by correlating the LR MS image patches with the LR PAN dictionary. The HRMS is formed by convolving the Sparse coefficients with the HR PAN dictionary. The world view -2 satellite images (HRPAN and LRMS) of Madurai, Tamil Nadu are used to test the proposed method. The experimental results show that this method can well preserve spectral and spatial details of the input images by adaptive learning. While compared to other well-known methods the proposed method offers high quality results to the input images by providing 87.28% Quality with No Reference (QNR).

查看原文本刊更多论文

基于压缩采样匹配追踪(CoSaMP)方法的卫星图像融合

低分辨率多光谱(LRMS)图像与高分辨率全色(HRPAN)图像的融合是遥感领域的一个重要课题。本文研究了数据稀疏表示的卫星图像融合问题。利用基于正交匹配追踪(OMP)算法的压缩采样匹配追踪(CoSaMP)对HRPAN和LRMS图像进行稀疏重建，得到高分辨率的MS图像。稀疏系数是通过将LR MS图像块与LR PAN字典相关联得到的。通过将稀疏系数与HR PAN字典进行卷积形成HRMS。使用泰米尔纳德邦马杜赖的世界-2卫星图像(HRPAN和LRMS)来测试所提出的方法。实验结果表明，该方法通过自适应学习可以很好地保留输入图像的光谱和空间细节。与其他已知的方法相比，该方法可以提供87.28%的无参考质量(QNR)，从而获得高质量的输入图像。

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

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)

自引率

0.00%

发文量