多源图像融合技术在无人机中的应用
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摘要
随着无人机技术的发展,无人机多源图像融合是一个日益受到关注的研究领域。在研究传统算法的基础上,针对经典HIS变换融合算法产生的光谱失真问题,通过引入压缩感知技术对传统算法进行改进,提出了一种融入压缩感知技术的图像融合算法并应用到无人机数码照片和SAR图像融合处理中。实验表明,该方法在引入SAR图像纹理信息的同时,能够有效保持可见光图像光谱信息,取得了较好的融合效果。
With the development of UAV technology,the multi-source image fusion in UAV has attracted more and more attention.On the basis of traditional algorithms,an image fusion algorithm incorporating compressed sensing technology is proposed to solve the problem of spectral distortion caused by the classical HIS transform fusion algorithm.By introducing compressed sensing technology,the traditional algorithm is improved.The image fusion algorithm is applied to the fusion of UAV digital photos and SAR images.The experiment results show that this method can effectively preserve the spectral information of visible light image while introducing texture information of SAR image,and achieve good fusion effect.
With the development of UAV technology,the multi-source image fusion in UAV has attracted more and more attention.On the basis of traditional algorithms,an image fusion algorithm incorporating compressed sensing technology is proposed to solve the problem of spectral distortion caused by the classical HIS transform fusion algorithm.By introducing compressed sensing technology,the traditional algorithm is improved.The image fusion algorithm is applied to the fusion of UAV digital photos and SAR images.The experiment results show that this method can effectively preserve the spectral information of visible light image while introducing texture information of SAR image,and achieve good fusion effect.
引文
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[3] JIAO Long .Single Remote Sensing Image Dehazing[J].Geoscience and Remote Sensing Letters IEEE,2014,11(1):59-63.
[4] DAILY M I,FARR T,ELACHI C,et al.Geologic Interpretation from Composited Radar and Landsat Imagery[J].Photogrammetric Engineering and Remote Sensing,1979,45(8):1109-1116.
[5] POHL C,VAN GGENDEREN J L.Multisensor Image Fusion in Remote Sensing:Concepts,Methods and Applications[J].International Journal of Remote Sensing,1998,19(5):823-854.
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[7] 殷立琼,姚刚 .基于 HIS 和小波变换的 IKONOS 影像融合[J].现代测绘,2009,32(2):15-17.
[8] 延翔,秦翰林,刘上乾,等.基于 Tetrolet 变换的图像融合[J].光电子.激光,2013,24(8):1629-1633.
[9] KOTWAL K,CHAUDHURI S.A Bayesian Approach to Visualization-oriented Hypers-pectral Image Fusion[J].Information Fusion,2013,14(4):349-360.
[10] ZHANG B H,ZHANG C T,WU J S,et al.A Medical Image Fusion Method Based on Energy Classification of BEMD Components[J].Optik-International Journal for Light and Electron Optics,2014,125(1):146-153.
[11] HUA K L,WANG H C,RUSDI A H,et al.A Novel Multi-focus Image Fusion Algor-ithm Based on Random Walks[J].Journal of Visual Communication and Image Representation,2014,25(5):951-962.
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[13] 王霖郁,蒋强卫,李爽.基于双目图像多特征点融合匹配物体识别与定位研究[J].无线电工程,2018,48(8):628-633
[14] 王春龙,马传焱,时荔蕙,等.一种用于高速无人机的对地目标捕获和识别方法[J].无线电工程,2016,46(3):37-40.
[15] 裴璐乾.SAR、红外、可见光图像配准及融合算法研究[D].西安:西安电子科技大学,2011.
[16] 杨金库,郭雷,杨宁,等.多光谱图像与全色图像融合[J].西安工业大学学报,2013,33(8):685-688.
[17] 李玲玲.像素级图像融合方法与应用[M].甘肃:甘肃人民出版社,2006.
[18] 李洋.红外图像与可见光图像融合研究[D].哈尔滨:哈尔滨工程大学,2013.