HCT变换与联合稀疏模型相结合的遥感影像融合

许宁; 肖新耀; 尤红建; 曹银贵

doi:10.11947/j.AGCS.2016.20150372

测绘学报 >

2016 , Vol. 45 >Issue 4: 434 - 441

DOI: https://doi.org/10.11947/j.AGCS.2016.20150372

摄影测量学与遥感

HCT变换与联合稀疏模型相结合的遥感影像融合

许宁 ,
肖新耀 ,
尤红建 ,
曹银贵

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1. 中国科学院空间信息处理与应用系统技术重点实验室, 北京 100190;
2. 中国科学院电子学研究所, 北京 100190;
3. 中国科学院大学, 北京 100049;
4. 中国地质大学(北京)土地科学技术学院, 北京 100083

许宁(1982-),男,博士生,研究方向为光学遥感影像配准、融合以及高光谱图像处理。

收稿日期: 2015-07-14

修回日期: 2015-12-16

网络出版日期: 2016-04-28

基金资助

中国地质调查局地质调查(1212011120226);国家863计划(2012AA12A308);中国科学院科技服务网络计划(KFJ-EW-STS-046)

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A Pansharpening Method Based on HCT and Joint Sparse Model

XU Ning ,
XIAO Xinyao ,
YOU Hongjian ,
CAO Yingui

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1. Key Laboratory of Technology in Geo-spatial Information Processing and Application System, IECAS, Beijing 100190, China;
2. Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China;
3. University of Chinese Academy of Sciences, Beijing 100049, China;
4. School of Land Science and Technology, China University of Geosciences, Beijing 100083, ChinaAbstract

Received date: 2015-07-14

Revised date: 2015-12-16

Online published: 2016-04-28

Supported by

The Geological Survey Program of China Geological Survey(No.1212011120226);The National High-tech Research and Development Program of China(863 Program)(No.2012AA12A308);The Science and Technology Services Network Program of Chinese Academy of Sciences(No.KFJ-EW-STS-046)

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摘要

提出了一种基于HCT变换和联合稀疏模型的遥感影像融合方法,可更有效地利用多光谱所需谱段的光谱信息,最终得到所需谱段的融合影像。该方法将所需谱段的多光谱影像进行HCT变换,获取其亮度分量和角度分量;然后利用亮度分量和全色影像小波变换的低频分量进行联合稀疏模型的构建、系数求解和融合,得到融合的全色低频分量;最后将该低频分量与前面步骤所得其他分量分别进行小波逆变换和HCT逆变换,得到高质量的融合影像。试验利用Pleiades-1和WorldView-2两种卫星数据进行验证,并通过视觉效果和量化的融合评价指标进行对比和分析,验证了本文算法的有效性。

关键词： 影像融合; 联合稀疏模型; HCT变换; 全色图像; 多光谱图像

本文引用格式

许宁 , 肖新耀 , 尤红建 , 曹银贵 . HCT变换与联合稀疏模型相结合的遥感影像融合[J]. 测绘学报, 2016 , 45(4) : 434 -441 . DOI: 10.11947/j.AGCS.2016.20150372

Abstract

A novel fusion method based on the hyperspherical color transformation (HCT) and joint sparsity model is proposed for decreasing the spectral distortion of fused image further. In the method, an intensity component and angles of each band of the multispectral image is obtained by HCT firstly, and then the intensity component is fused with the panchromatic image through wavelet transform and joint sparsity model. In the joint sparsity model, the redundant and complement information of the different images can be efficiently extracted and employed to yield the high quality results. Finally, the fused multi spectral image is obtained by inverse transforms of wavelet and HCT on the new lower frequency image and the angle components, respectively. Experimental results on Pleiades-1 and WorldView-2 satellites indicate that the proposed method achieves remarkable results.

Key words： image fusion; joint sparsity model; HCT; panchromatic image; multispectral image

参考文献

[1] THOMAS C, RANCHIN T, WALD L, et al. Synthesis of Multispectral Images to High Spatial Resolution:A Critical Review of Fusion Methods Based on Remote Sensing Physics[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(5):1301-1312.
[2] CARPER W J, LILLESAND T M, KIEFER R W. The Use of Intensity-hue-saturation Transformations for Merging SPOT Panchromatic and Multispectral Image Data[J]. Photogrammetric Engineering & Remote Sensing, 1990, 56(4):459-467.
[3] CHAVEZ JR P S, KWARTENG A Y. Extracting Spectral Contrast in Landsat Thematic Mapper Image Data Using Selective Principal Component Analysis[J]. Photogrammetric Engineering & Remote Sensing, 1989, 55(3):339-348.
[4] AIAZZI B, BARONTI S, SELVA M. Improving Component Substitution Pansharpening through Multivariate Regression of MS+Pan Data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(10):3230-3239.
[5] CHOI J, YU K, KIM Y. A New Adaptive Component-Substitution-based Satellite Image Fusion by Using Partial Replacement[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(1):295-309.
[6] GARGUET-DUPORT B,GIREL J,CHASSERY J M,et al. The Use of Multiresolution Analysis and Wavelets Transform for Merging SPOT Panchromatic and Multispectral Image Data[J]. Photogrammetric Engineering & Remote Sensing, 1996, 62(9):1057-1066.
[7] 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 and Remote Sensing, 2005, 43(10):2376-2385.
[8] El-MEZOUAR M C, KPALMA K, TALEB N, et al. A Pan-sharpening Based on the Non-subsampled Contourlet Transform:Application to Worldview-2 Imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(5):1806-1815.
[9] AIAZZI B, ALPARONE L, BARONTI S, et al. Context-driven Fusion of High Spatial and Spectral Resolution Images Based on Oversampled Multiresolution Analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(10):2300-2312.
[10] WANG Jian, XU Changhui, ZHANG Jixian, et al. An EMD-IHS Model for High Resolution Image Fusion[C]//JU Weimin, ZHAO Shuhe. Remotely Sensed Data and Information. Nanjing:[s.n.], 2007, 6752:675209.
[11] DONG Weihua, LI Xia'nen, LIN Xiangguo, et al. A Bidimensional Empirical Mode Decomposition Method for Fusion of Multispectral and Panchromatic Remote Sensing Images[J]. Remote Sensing, 2014, 6(9):8446-8467.
[12] LI Shutao, 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.
[13] CHEN S S, DONOHO D L, SAUNDERS M A. Atomic Decomposition by Basis Pursuit[J]. SIAM Review, 2001, 43(1):129-159.
[14] ZHU Xiaoxiang, BAMLER R. A Sparse Image Fusion Algorithm with Application to Pan-sharpening[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(5):2827-2836.
[15] CHENG Ming, WANG Cheng, LI Jie, et al. Sparse Representation Based Pansharpening Using Trained Dictionary[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(1):293-297.
[16] ZHANG Yun, HONG Gang. An IHS and Wavelet Integrated Approach to Improve Pan-sharpening Visual Quality of Natural Colour IKONOS and QuickBird Images[J]. Information Fusion, 2005, 6(3):225-234.
[17] 楚恒, 王汝言, 朱维乐. DCT域遥感影像融合算法[J]. 测绘学报, 2008, 37(1):70-76. CHU Heng, WANG Ruyan, ZHU Weile. Remote Sensing Image Fusion Algorithm in the DCT Domain[J]. Acta Geodaetica et Cartographica Sinica, 2008, 37(1):70-76.
[18] YANG Shuyuan, WANG Min, JIAO Licheng. Fusion of Multispectral and Panchromatic Images Based on Support Value Transform and Adaptive Principal Component Analysis[J]. Information Fusion, 2012, 13(3):177-184.
[19] 夏晓天, 王斌, 张立明. 基于推广的IHS变换和压缩传感的遥感图像融合[J]. 计算机辅助设计与图形学学报, 2013, 25(9):1399-1409. XIA Xiaotian, WANG Bin, ZHANG Liming. Remote Sensing Image Fusion Based on Generalized IHS Transformation and Compressive Sensing[J]. Journal of Computer-Aided Design & Computer Graphics, 2013, 25(9):1399-1409.
[20] 吴连喜, 梁波, 刘晓梅, 等. 保持光谱信息的遥感图像融合方法研究[J]. 测绘学报, 2005, 34(2):118-122, 128. WU Lianxi, LIANG Bo, LIU Xiaomei, et al. A Spectral Preservation Fusion Technique for Remote Sensing Images[J]. Acta Geodaetica et Cartographica Sinica, 2005, 34(2):118-122, 128.
[21] PADWICK C, DESKEVICH M, PACIFICI F, et al. World View-2 Pan-sharpening[C]//Proceedings of the ASPRS 2010 Annual Conference. San Diego:[s.n.], 2010:26-30.
[22] CANDES E J, ROMBERG J K, TAO T. Stable Signal Recovery from Incomplete and Inaccurate Measurements[J]. Communications on Pure and Applied Mathematics, 2005, 59(8):1207-1233.
[23] DONOHO D L.For Most Large Underdetermined Systems of Equations, the Minimal-norm Near-solution Approximates the Sparest Near-solution[J]. Communications on Pure and Applied Mathematics, 2006, 59(7):907-934.
[24] BRUCKSTEIN A M, ELAD M, ZIBULEVSKY M. On the Uniqueness of Nonnegative Sparse Solutions to Underdetermined Systems of Equations[J]. IEEE Transactions on Information Theory, 2008, 54(11):4813-4820.
[25] DUARTE M F, SARVOTHAM S, BARON D, et al. Distributed Compressed Sensing of Jointly Sparse Signals[C]//Proceeding of Conference Record of the 39th Asilomar Conference on Signals, Systems and Computers. Pacific Grove:IEEE, 2005.
[26] AHARON M, ELAD M, BRUCKSTEIN A. K-SVD:An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation[J]. IEEE Transactions on Signal Processing, 2006, 54(11):4311-4322.
[27] AIAZZI B, BARONTI S, SELVA M, et al. Bi-cubic Interpolation for Shift-free Pan-sharpening[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 86:65-76.
[28] KHAN M M, CHANUSSOT J, CONDAT L, et al. Indusion:Fusion of Multispectral and Panchromatic Images Using the Induction Scaling Technique[J]. IEEE Geoscience and Remote Sensing Letters, 2008, 5(1):98-102.
[29] ZHOU J, CIVCO D L, SILANDER J A. A Wavelet Transform Method to Merge Landsat TM and SPOT Panchromatic Data[J]. International Journal of Remote Sensing, 1998, 19(4):743-757.
[30] KHAN M M, ALPARONE L, CHANUSSOT J. Pansharpening Quality Assessment Using the Modulation Transfer Functions of Instruments[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(11):3880-3891.
[31] 王慧贤, 江万寿, 雷呈强, 等. 光谱与空间局部相关的SVR影像融合方法[J]. 测绘学报, 2013, 42(4):508-515. WANG Huixian, JIANG Wanshou, LEI Chengqiang, et al. A Robust SVR Image Fusion Method Based on Local Spectral and Spatial Correlation[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(4):508-515.
[32] ALPARONE L, WALD L, CHANUSSOT J, et al. Comparison of Pansharpening Algorithms:Outcome of the 2006 GRS-S Data-fusion Contest[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(10):3012-3021.
[33] ALPARONE L, BARONTI S, GARZELLI A, et al. A Global Quality Measurement of Pan-sharpened Multispectral Imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2004, 1(4):313-317.
[34] 刘婷, 程建. 小波变换和稀疏表示相结合的遥感图像融合[J]. 中国图象图形学报, 2013, 18(8):1045-1053. LIU Ting, CHENG Jian. Remote Sensing Image Fusion with Wavelet Transform and Sparse Representation[J]. Journal of Image and Graphics, 2013, 18(8):1045-1053.

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