一种基于KPCA和Brovey变换的遥感影像融合方法
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摘要
针对PCA-Brovey算法在融合多光谱遥感影像(MS)和全色影像(PAN)方面存在的空间细节特征和光谱特征不能同时得到较大提高的问题,提出一种基于KPCA-Brovey的遥感影像融合方法。利用核主成分变换(KPCA)算法的非线性光谱数据挖掘的特性,提取多光谱影像中信息量最大的3个主分量KPC1、KPC2和KPC3;然后利用Brovey算法对3个主分量和全色波段进行归一化融合运算,使融合结果中的空间信息和光谱信息更丰富。利用武汉区的Landsat5-TM和佛山区的Quick Bird影像进行实验,与PCA-Brovey方法相比,该方法在空间信息和光谱信息方面有明显提高,其中光谱信息提取率分别提高到96.6%(武汉区)和96.1%(佛山区),光谱相关系数提高到0.907 9(武汉区)和0.897 9(佛山区)。
Aiming at the problem that the spatial details and spectral features of PCA-Brovey algorithm could not be improved simultaneously in fusing multi-spectral remote sensing images( MS) and panchromatic images( PAN),a remote sensing image fusion method based on KPCA-Brovey was proposed. Firstly,by utilizing the characteristics of non-linear spectral data mining based on Kernel Principal Component Transform( KPCA) algorithm,three principal components including KPC1,KPC2 and KPC3 with the largest amount of information in multispectral images were extracted. Then,three principal components and panchromatic bands were normalized by Brovey algorithm to enrich the spatial and spectral information in the fusion results. Finally,the experimental results with two satellites data,from Landsat5-TM( Wuhan District) and Quick Bird( Foshan District),indicated that the proposed method obviously outperformed the PCA-Brovey method in spectral and spatial information extracting. The spectral information extracting rates were increased to 96. 6 %( Wuhan District) and 96.1%( Foshan District) respectively. The spectral correlation coefficients were increased to 0.9079( Wuhan District) and 0.8979( Foshan District) respectively.
Aiming at the problem that the spatial details and spectral features of PCA-Brovey algorithm could not be improved simultaneously in fusing multi-spectral remote sensing images( MS) and panchromatic images( PAN),a remote sensing image fusion method based on KPCA-Brovey was proposed. Firstly,by utilizing the characteristics of non-linear spectral data mining based on Kernel Principal Component Transform( KPCA) algorithm,three principal components including KPC1,KPC2 and KPC3 with the largest amount of information in multispectral images were extracted. Then,three principal components and panchromatic bands were normalized by Brovey algorithm to enrich the spatial and spectral information in the fusion results. Finally,the experimental results with two satellites data,from Landsat5-TM( Wuhan District) and Quick Bird( Foshan District),indicated that the proposed method obviously outperformed the PCA-Brovey method in spectral and spatial information extracting. The spectral information extracting rates were increased to 96. 6 %( Wuhan District) and 96.1%( Foshan District) respectively. The spectral correlation coefficients were increased to 0.9079( Wuhan District) and 0.8979( Foshan District) respectively.
引文
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[20]张小利,李雄飞,李军.融合图像质量评价指标的相关性分析及性能评估[J].自动化学报,2014,40(2):306-315.ZHANG Xiaoli,LI Xiongfei,LI Jun. Validation and correlation analysis of metrics for evaluating performance of image fusion[J]. Acta Automatica Sinica,2014,40(2):306-315.
[2]翁永玲,田庆久.遥感数据融合方法分析与评价综述[J].遥感信息,2003,2(3):49-54.WENG Yongling,TIAN Qingjiu. Analysisand evaluation of method on remote sensing data fusion[J]. Remote Sensing Information,2003,2(3):49-54.
[3] VRABEL J. Multispectral imagery advanced band sharpening study[J]. Photogrammetric Engineering&Remote Sensing,2000,66(1):73-80.
[4]CARPER W J,LILLESAND T M,KIEFER P W. The use of intensity-hue-saturation transformation for merging SPOT panchromatic and multispectral image data[J]. Photogrammetric Engineering&Remote Sensing,1990,56(4):459-467.
[5]JR P S C,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.
[6]胥兵,方臣. ZY-102C星图像与ETM+图像融合方法及效果评价[J].国土资源遥感,2014,26(3):80-85.XU Bing,FANG Chen. Datafusion methods of ZY-102C and ETM+images and effect evaluation[J]. Remote Sensing for Land and Resources,2014,26(3):80-85.
[7]余汪洋,陈祥光,董守龙,等.基于小波变换的图像融合算法研究[J].北京理工大学学报,2014,34(12):1262-1266.YU Wangyang,CHEN Xiangguang,DONG Shoulong,et al. Study on image fusion algorithm based on wavelet transform[J]. Transactions of Beijing Institute of Technology,2014,34(12):1262-1266.
[8]EI-MEZOUAR M C,KPALMA K,TALEB,et al. A pan-sharpening based on the non-sub sampled contourlet transform:application to world-2 imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing,2014,7(5):1806-1815.
[9] AIAZZI B,ALPARONE L,BARONTI S,et al. Contex-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis[J]. IEEE Transaction on Geoscience and Remote Sensing,2002,40(10):2300-2312.
[10]李艳雯,杨英宝,程三胜.基于亮度平滑滤波调节的SPOT5影像融合[J].遥感信息,2007(1):63-66.LI Yanwen,YANG Yingbao,CHENG Sansheng. The Spot-5 image fusion by smoothing filter-based intensity modulation(sfim)[J].Remote Sensing Information,2007(1):63-66.
[11] KALLEL A. MTF-Adjusted pansharpening approach based on coupled multiresolution decomposition[J]. IEEE Transactions on Geoscience&Remote Sensing,2016,53(6):3124-3145
[12]QU J,LI Y,DONG W. Hyperspectral pansharpening with guided filter[J]. IEEE Geoscience&Remote Sensing Letters,2017,14(11):2152-2156.
[13]许宁,肖新耀,尤红建等,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.XU Ning,XIAO Xinyao,YOU Hongjian,et al. Apansharpening method based on HCT and joint sparse model[J]. Acta Geodaetic et Cartographic Sinica,2016,45(4):434-441.
[14] LEPRIEUR C,KERR Y H,MASTORCHIO S,et al. Monitoring vegetation cover across semi-arid regions comparison of remote observations from various scales[J]. International Journal of Remote Sensing,2000,21(2):281-300.
[15]TU T M,LEE Y C,CHANG C P,et al. Adjustable intensity-huesaturation and Brovey transform fusion technique for ikonos/quickbird imagery[J]. Optical Engineering,2005,44(11):116201-116210.
[16]沈明霞,何瑞银,丛静华,等.基于主成分分析与Brovey变换的ETM+影像植被信息提取[J].农业机械学报,2007,38(9):87-89.SHEN Mingxia, HE Ruiyin, CONG Jinghua, et al. Study on extraction of vegetation information of ETM+by using PCA method and Brovey transform[J]. Transaction of the Chinese Society of Agriculture Machinery,2007,38(9):87-89.
[17] GHARBIA R,BAZ A H E,HASSANIEN A E,et al. Remote sensing image fusion approach based on Brovey and wavelets transforms[C]//Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA2014, Advances in Intelligent Systems and Computing 303.Switzerland:Springer International Publishing,2014:311-321.
[18]ZHOU Ji. A wavelet transform method to merge Landsat ETM and spot panchromatic data[J]. International Journal of Remote Sensing,1998,19(4):743-757.
[19]刘文杰,李卫军,李浩光,等,基于KPCA和近红外光谱的鉴别玉米单倍体方法研究[J].光谱学与光谱分析,2017,37(7):2024-2027.LIU Wenjie,LI Weijun,LI Haoguang,et al. Research on the method of identifying maize haploid based on KPCA and near infrared spectrum[J]. Spectroscopy and Spectral Analysis,2017,37(7):2024-2027.
[20]张小利,李雄飞,李军.融合图像质量评价指标的相关性分析及性能评估[J].自动化学报,2014,40(2):306-315.ZHANG Xiaoli,LI Xiongfei,LI Jun. Validation and correlation analysis of metrics for evaluating performance of image fusion[J]. Acta Automatica Sinica,2014,40(2):306-315.
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