基于山区GF-1号遥感影像融合及质量评价
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摘要
针对山地地形高差较大,地形复杂且存在较多阴影现象的特点,选取国产GF-1号卫星数据,选取南昌城区西部某山区作为实验研究区域,采用HSV变换、CN变换、Gram-Schmidt融合、PC Spectral锐化、最近邻域扩散锐化等五种融合方法来对山地地区遥感图像进行融合实验,并结合定性与定量指标对融合结果进行质量评价,以期优选出适合山地的GF-1号卫星数据图像融合处理方法,为基于山地国产高分数据的融合应用提供一定的参考价值。实验结果表明:Gram-Schmidt融合和最近邻域扩散锐化融合方法的融合效果表现最佳。
In view of the large difference in mountain terrain, complex terrain and more shadows, the domestic GF-1 satellite data was selected, and a mountainous area in the western part of Nanchang City was selected as the experimental research area, using HSV transformation, CN transformation, Gram-Schmidt fusion, Five fusion methods, such as PC Spectral sharpening and NNDiffuse Pan Sharpening, are used to fuse remote sensing images in mountainous areas, and qualitative and quantitative indicators are used to evaluate the quality of fusion results in order to optimize the image fusion processing of GF-1 satellite data suitable for mountainous areas. The method provides a certain reference value for the fusion application based on mountain domestic high score data. The experimental results show that the fusion effect of Gram-Schmidt fusion and NNDiffuse Pan Sharpening fusion method is the best.
In view of the large difference in mountain terrain, complex terrain and more shadows, the domestic GF-1 satellite data was selected, and a mountainous area in the western part of Nanchang City was selected as the experimental research area, using HSV transformation, CN transformation, Gram-Schmidt fusion, Five fusion methods, such as PC Spectral sharpening and NNDiffuse Pan Sharpening, are used to fuse remote sensing images in mountainous areas, and qualitative and quantitative indicators are used to evaluate the quality of fusion results in order to optimize the image fusion processing of GF-1 satellite data suitable for mountainous areas. The method provides a certain reference value for the fusion application based on mountain domestic high score data. The experimental results show that the fusion effect of Gram-Schmidt fusion and NNDiffuse Pan Sharpening fusion method is the best.
引文
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[2] HE Weiji,FENG Weiyi, PENG Yiyue, et al.Multilevel Image Fusion and Enhancement for Target Detection[J].International Journal for Light and Electron Optics,2015(126):11-12.
[3] 翟梦.天地图·福建数据融合技术与方法[J].测绘与空间地理信息,2015(10):184-186.
[4] 王爱芸.山地GF-2卫星遥感图像融合方法优选研究[D].云南昆明:云南大学,2016.
[5] ARDESHIR A. Image fusion: Advances in the State of the Art[J]. Information Fusion,2006,8(2):199-211.
[6] YANG Bin, LI Shutao.Pixel-level Image Fusion with Simultaneous Orthogonal Matching Pursuit[J]. Information Fusion,2010,13(1):188-191.
[7] 赵丹,戴文战,李俊峰.基于NSST和改进PCNN的医学图像融合[J].光电子·激光,2018(1):95-104.
[8] 蔡彩.基于高分辨率遥感影像的城市不透水层提取研究进展浅析[J].北京测绘,2018,32(9):1007-1014.
[9] 彭文建,郭云开,张晓红,等.基于MATLAB算法的遥感图像融合[J].测绘与空间地理信息,2012(3):30-32.
[10] 邓良,程先富,谢金红,等.SPOT5影像数据不同融合方法的比较与评价[J].测绘与空间地理信息,2012(3):19-23.
[11] HUANG Xiande,ZHOU Qun,WANG Xing. Fusion of Resources Satellite-1 Remote Sensing Panchromatic and Multispectral Images[J]. Bulletin of Surveying and Mapping,2015(1):109-114.
[12] 吴松,安裕伦,刘绥华,等.无人机高分辨率数据与Landsat 8多光谱数据的图像融合研究分析[J].贵州师范大学学报(自然科学版),2015(1):13-17.
[13] 陈业培,孙开敏,尹杰,等.高分二号影像融合方法质量评价[J].测绘科学,2017,42(11):35-40.
[14] 董崧,臧淑英,吴长山,等.SPOT-7遥感图像融合技术对比研究[J].测绘与空间地理信息,2017(1):75-78.
[15] 纪峰,李泽仁,常霞,等.基于PCA和NSCT变换的遥感图像融合方法[J].图学学报,2017,38(2):247-252.
[16] 常化文,陈春香.基于HSV变换与小波变换的遥感图像融合[J].计算机工程与设计,2007(23):5682-5684.
[17] 王广杰,周介铭,杨存建,等.基于不同算法的遥感影像融合分析[J].四川师范大学学报(自然科学版),2011,34(2):255-259.
[18] 刘锟,付晶莹,李飞.高分一号卫星4种融合方法评价[J].遥感技术与应用,2015(5):980-986.
[19] 樊旭艳,付春龙,石继海.基于主成分分析的遥感图像模拟真彩色融合法[J]. 测绘科学技术学报,2006,23(4):287-289.
[20] SUN Weihua,CHEN Bin, MESSINGER W.Nearest-neighbor Diffusion-based Pan Sharpening Algorithm for Spectral Images[J].Optical Engineering,2014,53 (1):013107-013117.
[21] 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(2):306-315.
[22] 余先川,裴文静.针对不同融合算法的质量评价指标性能评估[J]. 红外与激光工程,2012(12):3416-3422.