基于分布式压缩感知的遥感图像融合算法
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摘要
针对基于压缩感知(Compressed Sensing,CS)理论的传统遥感图像融合算法未能考虑源图像信息相关性的特点,该文提出一种基于分布式压缩感知(Distributed Compressed Sensing,DCS)的遥感图像融合改进算法。通过DCS的第1联合稀疏模型(Joint Sparsity Model-1,JSM-1)提取源图像低频信息的公共部分和独有部分,再利用独有特征添加(UFA)的融合规则进行融合,从而提高融合精度。选取Quick Bird卫星实测图像数据对该文方法和多个传统融合方法进行仿真实验并进行评价指标的对比,结果表明该文方法融合性能相对传统遥感图像融合方法都有不同程度的提高。
The conventional Compressed Sensing(CS) based remote sensing image fusion algorithm does not consider the correlation between the source images. In this paper, a novel Distributed CS(DCS) based remote sensing image fusion algorithm is proposed to address the correlation between the source images. The proposed algorithm extracts the common part and the unique part of the low frequency information of the source images, in the framework of Joint Sparsity Model-1(JSM-1). The Unique Feature Addition(UFA) rule is then used to improve the fusion performance. In the experiments, the Quick Bird images are utilized to evaluate the performance of the proposed algorithm. The experimental results demonstrate that the fusion performance is significantly improved using the proposed algorithm, compared with several classical fusion algorithms.
The conventional Compressed Sensing(CS) based remote sensing image fusion algorithm does not consider the correlation between the source images. In this paper, a novel Distributed CS(DCS) based remote sensing image fusion algorithm is proposed to address the correlation between the source images. The proposed algorithm extracts the common part and the unique part of the low frequency information of the source images, in the framework of Joint Sparsity Model-1(JSM-1). The Unique Feature Addition(UFA) rule is then used to improve the fusion performance. In the experiments, the Quick Bird images are utilized to evaluate the performance of the proposed algorithm. The experimental results demonstrate that the fusion performance is significantly improved using the proposed algorithm, compared with several classical fusion algorithms.
引文
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[2]RAHMANI S,STRAIT M,MERKURJEV D,et al.An adaptive IHS pan-sharpening method[J].IEEE Geoscience and Remote Sensing Letters,2010,7(4):746-750.doi:10.1109/LGRS.2010.2046715.
[3]OTAZU X,GONZALEZ-AUDICANA M,FORS O,et al.Introduction of sensor spectral response into image fusion methods.Application to wavelet-based methods[J].IEEE Transactions on Geoscience&Remote Sensing,2005,43(10):2376-2385.doi:10.1109/TGRS.2005.856106.
[4]AIAZZI B,ALPARONE L,BARONTI S,et al.MTFtailored multiscale fusion of high-resolution MS and Pan imagery[J].Photogrammetric Engineering&Remote Sensing,2006,72(5):591-596.doi:10.14358/PERS.72.5.591.
[5]金益如,杨学志,董张玉,等.一种NSST与稀疏表示相结合的遥感图像融合算法[J].地理与地理信息科学,2016,32(2):60-66.doi:10.3969/j.issn.1672-0504.2016.02.012.JIN Yiru,YANG Xuezhi,DONG Zhangyu,et al.A new algorithm for remote sensing image fusion based on NSST and sparse representation[J].Geography and GeoInformation Science,2016,32(2):60-66.doi:10.3969/j.issn.1672-0504.2016.02.012.
[6]ZHU Fuzhen,HE Hongchang,WANG Xiaofei,et al.A new multi-spectral image fusion algorithm based on compressive sensing[C].The Fifth International Conference on Instrumentation&Measurement,Computer,Communication and Control,Qinhuangdao,2015:1904-1908.doi:10.1109/IMCCC.2015.404.
[7]VICINANZA M R,RESTAINO R,VIVONE G,et al.A pansharpening method based on the sparse representation of injected details[J].IEEE Geoscience and Remote Sensing Letters,2015,12(1):180-184.doi:10.1109/LGRS.2014.2331291.
[8]DUARTE M F,SARVOTHAM S,BARON D,et al.Distributed compressed sensing of jointly sparse signals[C].Conference Record of the Thirty-Ninth Asilomar Conference on Signals,Systems and Computers,Pacific Grove,California,2005:1537-1541.doi:10.1109/ACSSC.2005.1600024.
[9]黄立勤,陈财淦.全景图拼接中图像融合算法的研究[J].电子与信息学报,2014,36(6):1292-1298.doi:10.3724/SP.J.1146.2013.01220.HUANG Liqin and CHEN Caigan.Study on image fusion algorithm of panoramic image stitching[J].Journal of Electronics&Information Technology,2014,36(6):1292-1298.doi:10.3724/SP.J.1146.2013.01220.
[10]YIN Haitao and LI Shutao.Multimodal image fusion with joint sparsity model[J].Optical Engineering,2011,50(6):409-421.doi:10.1117/1.3584840.
[11]张颖超,茅丹,胡凯.压缩传感理论在心电图信号恢复问题上的研究[J].计算机研究与发展,2014,51(5):1018-1027.doi:10.7554/issn1000-1239.2014.20121161.ZHANG Yingchao,MAO Dan,and HU Kai.ECG signal recovery problem based on compressed sensing theory[J].Journal of Computer Research and Development,2014,51(5):1018-1027.doi:10.7554/issn1000-1239.2014.20121161.
[12]CANDES E and TAO T.Decoding by linear programming[J].IEEE Transactions on Information Theory,2005,51(12):4203-4215.doi:10.1109/TIT.2005.858979.
[13]DAI Qi and SHA Wei.The physics of compressive sensing and the gradient-based recovery algorithms[OL].http://arxiv.org/abs/0906.1487.2009.6.
[14]LI Shutao and YANG Bin.A new pan-sharpening method using a compressed sensing technique[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(2):738-746.doi:10.1109/TGRS.2010.2067219.
[15]VIVONE G,ALPARONE L,CHANUSSOT J,et al.A critical comparison among pansharpening algorithms[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(5):2565-2586.doi:10.1109/TGRS.2014.2361734.
[16]RANCHIN T and WALD L.Fusion of high spatial and spectral resolution images:The ARSIS concept and its implementation[J].Photogrammetric Engineering&Remote Sensing,2000,66:49-61.