改进的基于稀疏表示的全色锐化算法
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摘要
为了更有效地结合高分辨率全色(PAN)图像细节信息和低分辨率多光谱(MS)图像光谱信息,提出了一种改进的全色锐化算法。首先,对低分辨率MS图像的强度通道进行下采样再上采样获取其低频成分;其次,用强度通道减去低频成分获取其高频成分,在获取到的高低频成分中进行随机采样来构建字典;然后,用构建好的过完备字典对高分辨率PAN图像进行分块分解以获取高频信息;最后,将分解出的高频信息注入到低分辨率MS图像中以重建高分辨率MS图像。经多组实验后发现,所提出的算法在主观上保留了光谱信息,并注入了大量的空间细节信息。对比结果表明,相比其他诸如基于成分替换算法、基于多分辨率分析算法、基于稀疏表示算法,所提算法重建出来的高分辨率MS图像更加清晰,且在相关系数等多种客观评价指标上优于对比算法。
In order to more effectively combine the detail information of high resolution PANchromatic(PAN)image and the spectral information of low resolution MultiSpectral(MS)image,an improved panchromatic sharpening algorithm based on sparse representation was proposed.Firstly,the intensity channel of an MS image was down-sampled and then up-sampled to get its low-frequency components.Secondly,the MS image intensity channel minus low-frequency components to obtain its high-frequency components.Random sampling was performed in the acquired high and low frequency components to construct a dictionary.Thirdly,the PAN image was decomposed to get the high-frequency components by using the constructed overcomplete dictionary.Finally,the high-frequency components of the PAN image were injected into the MS image to obtain the desired high-resolution MS image.After a number of experiments,it was found that the proposed algorithm subjectively retains the spectral information and injects a large amount of spatial details.Compared with component substitution method,multiresolution analysis method and sparse representation method,the reconstructed high resolution MS image by the proposed algorithm is more clear,and the correlation coefficient and other objective evaluation indicators of the proposed algorithm are also better.
In order to more effectively combine the detail information of high resolution PANchromatic(PAN)image and the spectral information of low resolution MultiSpectral(MS)image,an improved panchromatic sharpening algorithm based on sparse representation was proposed.Firstly,the intensity channel of an MS image was down-sampled and then up-sampled to get its low-frequency components.Secondly,the MS image intensity channel minus low-frequency components to obtain its high-frequency components.Random sampling was performed in the acquired high and low frequency components to construct a dictionary.Thirdly,the PAN image was decomposed to get the high-frequency components by using the constructed overcomplete dictionary.Finally,the high-frequency components of the PAN image were injected into the MS image to obtain the desired high-resolution MS image.After a number of experiments,it was found that the proposed algorithm subjectively retains the spectral information and injects a large amount of spatial details.Compared with component substitution method,multiresolution analysis method and sparse representation method,the reconstructed high resolution MS image by the proposed algorithm is more clear,and the correlation coefficient and other objective evaluation indicators of the proposed algorithm are also better.
引文
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[23]WANG Z,BOVIK A C.A universal image quality index[J].IEEE Signal Process Letters,2002,9(3):81-84.
[24]YUHAS R H,GOETZ A F H,BOARDMAN J W.Discrimination among semi-arid landscape endmembers using the Spectral Angle Mapper(SAM)algorithm[C]//Proceedings of the 1992 Summaries of the Third Annual JPL Airborne Geoscience Workshop.Pasadena:JPL,1992:147-149.
[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.
[3]CHOI J,YU K,KIM Y.A new adaptive component-substitutionbased satellite image fusion by using partial replacement[J].IEEETransactions on Geoscience and Remote Sensing,2011,49(1):295-309.
[4]EL-MEZOUAR M C,TALEB N,KPALMA K,et al.An IHS-based fusion for color distortion reduction and vegetation enhancement in IKONOS imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(5):1590-1602.
[5]LEUNG Y,LIU J,ZHANG J.An improved adaptive intensity-hue-saturation method for the fusion of remote sensing images[J].IEEE Geoscience and Remote Sensing Letters,2014,11(5):985-989.
[6]王晓艳,刘勇,蒋志勇.一种基于结构相似度的IHS变换融合算法[J].遥感技术与应用,2011,26(5):670-676.(WANG X Y,LIU Y,JIANG Z Y.An HIS fusion method based on structural similarity[J].Remote Sensing Technology and Application,2011,26(5):670-676.)
[7]KANG X,LI S,BENEDIKTSSON J A.Pansharpening with matting model[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(8):5088-5099.
[8]NUNEZ J,OTAZU X,FORS O,et al.Multiresolution-based image fusion with additive wavelet decomposition[J].IEEE Transactions on Geoscience and Remote Sensing,1999,37(3):1204-1211.
[9]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.
[10]LI S,KWOK J.T,WANG Y.Using the discrete wavelet frame transform to merge Landsat TM and SPOT panchromatic images[J].Information Fusion,2002,3(1):17-23.
[11]JIN B,KIM G,CHO N I.Wavelet-domain satellite image fusion based on a generalized fusion equation[J].Journal of Applied Remote Sensing,2014,8(1):080599.
[12]曾宇燕,何建农.基于区域小波统计特征的遥感图像融合方法[J].计算机工程,2011,37(19):198-200.(ZENG Y Y,HE JN.Remote sensing image fusion method based on regional wavelet statistical features[J].Computer Engineering,2011,37(19):198-200.)
[13]ZHENG S,SHI W-Z,LIU J,et al.Remote sensing image fusion using multiscale mapped LS-SVM[J].IEEE Transactions on Geoscience and Remote Sensing,2008,46(5):1313-1322.
[14]LEE J,LEE C.Fast and efficient panchromatic sharpening[J].IEEE Transactions on Geoscience and Remote Sensing,2010,48(1):155-163.
[15]AIAZZI B,ALPARONE L,BARONTI S,et al.MTF-tailored multiscale fusion of high-resolution MS and Pan imagery[J].Photogrammetric Engineering and Remote Sensing,2006,72(5):591-596.
[16]KAPLAN N H,ERER I.Bilateral filtering-based enhanced pansharpening of multispectral satellite images[J].IEEE Geoscience and Remote Sensing Letters,2014,11(11):1941-1945.
[17]LI S,YANG B.A new PAN-sharpening method using a compressed sensing technique[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(2):738-746.
[18]YIN H.Sparse representation based pansharpening with details injection model[J].Signal Processing,2015,113:218-227.
[19]ZHU X X,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.
[20]GUO M,ZHANG H,LI J,et al.An online coupled dictionary learning approach for remote sensing image fusion[J].IEEETransactions on Geoscience and Remote Sensing,2014,7(4):1284-1294.
[21]WALD 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.
[22]WALD L.Quality of high resolution synthesized images:is there a simple criterion?[C]//Proceedings of the 2000 International Conference Fusion Earth Data.Piscataway,NJ:IEEE,2000:99-105.
[23]WANG Z,BOVIK A C.A universal image quality index[J].IEEE Signal Process Letters,2002,9(3):81-84.
[24]YUHAS R H,GOETZ A F H,BOARDMAN J W.Discrimination among semi-arid landscape endmembers using the Spectral Angle Mapper(SAM)algorithm[C]//Proceedings of the 1992 Summaries of the Third Annual JPL Airborne Geoscience Workshop.Pasadena:JPL,1992:147-149.