高分二号卫星影像融合及质量评价

doi:10.6046/gtzyyg.2016.04.17

摘要
参考文献
相关文章
Metrics

全文: PDF(5589 KB)

HTML
输出: BibTeX | EndNote (RIS)

摘要

高分二号卫星（GF-2）是我国自主研制的首颗空间分辨率优于1 m的民用光学遥感卫星，配备有0.81 m空间分辨率的全色相机和3.24 m空间分辨率的多光谱相机。对比分析适合GF-2影像的融合方法对于提高其应用效果与扩大应用领域具有实际意义。针对东北地区2014年11月22日和27日成像的GF-2影像，分别采用主成分分析（principal component analysis，PCA）、GS （Gram-Schmidt）变换、modified-HIS （intensity hue saturation）变换、高通滤波方法（high pass filter，HPF）和超球体色彩空间变换（hyperspherical color space resolution merge，HCS）等5种融合方法对多光谱和全色数据进行融合。并对5种融合影像进行质量评价，首先采用目视分析方法进行定性评价，其次采用信息熵、平均梯度、相关系数和光谱扭曲度等统计学指标进行客观定量评价，最后对融合影像进行地物分类。结果表明，HCS与GS变换融合影像无论是在视觉还是在地物分类应用上都具有较好的效果，且没有波段数的限制，最适合GF-2影像融合；HPF方法对空间细节信息的增强仅次于HCS变换，但是其光谱保真度效果最差；PCA和modified-IHS变换融合效果比较适中，可以作为GF-2影像融合的候补方法。

	服务

	把本文推荐给朋友
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	杨青山
	张华

关键词 ：像元同质区域(PHR), 像元形状指数(PSI), 阈值, 高空间分辨率遥感图像

Abstract：

GaoFen-2 (GF-2) is the first sub-meter civilian optical remote sensing satellite of China configured with 0.81 m resolution panchromatic cameras and 3.24 m multi-spectral cameras. Researches on image fusion algorithm suitable for GF-2 would have great significance for improving the image quality and expanding the application scope of the satellite. Four GF-2 images covering Northeast China from November 22 to 27, 2014 were used in this paper. The authors compared the efficiency of five fusion algorithms, which include component transform (PCA), Gram-Schmidt (GS), modified-HIS transform, HPF and HCS transform algorithm. In order to quantitatively assess the quality of the fused images, the authors adopted the following steps: The authors first examined the visual qualitative result and then evaluated the correlation between the original multi-spectral and the fused images. The authors compared the fused image with the original image in degree of distortion and parts of the statistical parameters such as entropy, average grads and correlation coefficient of the various frequency bands. Finally, the authors performed a supervised classification for the fused images, and compared the accuracies of resulting images. The result shows that all the fusion techniques improve the resolution and the visual effect. The HCS and GS transform algorithm could not only achieve the best results but also have no limit to the number of bands, and hence it is the most suitable method for the GF-2 image fusion. The HPF method is next only to the HCS transform method in the spatial detail enhancement, but the spectral fidelity is the worst among the five image fusion algorithms. It is moderate for the performance of the PCA and modified-IHS transform method, and then these algorithms can provide backup for the GF-2 image fusion.

Key words： pixel homogeneous regions(PHR) pixel shape index(PSI) threshold high spatial resolution remotely sensed imagery

收稿日期: 2015-05-11 出版日期: 2016-10-20

TP79

基金资助:

中国地质调查局项目“东北界河地区国土资源遥感综合调查”（编号：1212011220106）、“东北边境地区基础地质遥感调查”（编号：12120115063201）和国家自然科学基金项目“利用PSInSAR监测非城市区域地面形变的关键技术研究”（编号：41001248）共同资助。

通讯作者: 董玉森(1976-),男,博士,主要从事地学遥感与国土资源遥感调查等方面的研究。Email:ysdong@cug.edu.cn。

作者简介: 孙攀(1989-),男,硕士研究生,主要从事地学遥感方面的研究。Email:sunpan822@126.com。

引用本文:

孙攀, 董玉森, 陈伟涛, 马娇, 邹毅, 王金鹏, 陈华. 高分二号卫星影像融合及质量评价[J]. 国土资源遥感, 2016, 28(4): 108-113.
SUN Pan, DONG Yusen, CHEN Weitao, MA Jiao, ZOU Yi, WANG Jinpeng, CHEN Hua. Research on fusion of GF-2 imagery and quality evaluation. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(4): 108-113.

链接本文:

https://www.gtzyyg.com/CN/10.6046/gtzyyg.2016.04.17 或 https://www.gtzyyg.com/CN/Y2016/V28/I4/108

[1] Pohl C,Van Genderen J L.Review article multisensor image fusion in remote sensing:Concepts,methods and applications[J].International Journal of Remote Sensing,1998,19(5):823-854.
[2] 王广亮,李英成,曾钰,等.ALOS数据像素级融合方法比较研究[J].测绘科学,2008,33(6):121-124. Wang G L,Li Y C,Zeng Y,et al. Comparison and analysis of pixel-level image fusion algorithms applicable to ALOS data[J].Science of Surveying and Mapping,2008,33(6):121-124.
[3] Kaczynski R,Donnay J P,Muller F.Satellite image maps of Warsaw in the scale 1:25,000[J].EARSeL Advances in Remote Sensing,1995,4(2):100-103.
[4] Zhang Y,Hong G.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.
[5] Tu T M,Hsu C L,Tu P Y,et al.An adjustable pan-sharpening approach for IKONOS/QuickBird/GeoEye-1/WorldView-2 imagery[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2012,5(1):125-134.
[6] Tu T M,Huang P S,Hung C L,et al.A fast intensity-hue-saturation fusion technique with spectral adjustment for IKONOS imagery[J].IEEE Geoscience and Remote Sensing Letters,2004,1(4):309-312.
[7] Nikolakopoulos K G.Comparison of four different fusion techniques for IKONOS data[C]//Proceedings of 2004 IEEE International on Geoscience and Remote Sensing Symposium.Anchorage,Alaska,USA:IEEE,2004,4:2534-2537.
[8] 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.doi:10.6046/gtzyyg.2014.03.13. Xu B,Fang C.Data fusion methods of ZY-102C and ETM+ images and effect evaluation[J].Remote Sensing for Land and Resources,2014,26(3):80-85.doi:10.6046/gtzyyg.2014.03.13.
[9] Huang X,Wen D W,Xie J F,et al.Quality assessment of panchromatic and multispectral image fusion for the ZY-3 satellite:From an information extraction perspective[J].IEEE Geoscience and Remote Sensing Letters,2014,11(4):753-757.
[10] 云成.高分二号卫星[J].卫星应用,2014(9):65. Yun C.GF-2 satellite[J].Satellite Application,2014(9):65.
[11] Chavez P S,Berlin G L,Sowers L B.Statistical method for selecting Landsat MSS ratios[J].Journal of Applied Photographic Engineering,1982,8(1):23-30.
[12] Yésou H,Besnus Y,Rolet J.Extraction of spectral information from Landsat TM data and merger with SPOT panchromatic imagery-a contribution to the study of geological structures[J].ISPRS Journal of Photogrammetry and Remote Sensing,1993,48(5):23-36.
[13] Shettigara V K.A generalized component substitution technique for spatial enhancement of multispectral images using a higher resolution data set[J].Photogrammetric Engineering and Remote Sensing,1992,58(5):561-567.
[14] Laben C A,Brower B V.Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening:US,6011875[P].2000-01-04.
[15] 景娟娟,吕群波,周锦松,等.图像融合效果评价方法研究[J].光子学报,2007,36(S):313-317. Jing J J,Lyu Q B,Zhou J S,et al.Research on the assessment of fusion image[J].Acta Photonica Sinica,2007,36(S):313-317.
[16] Chavez P S Jr,Sides S C,Anderson J A.Comparison of three different methods to merge multiresolution and multispectral data:Landsat TM and SPOT panchromatic[J].Photogrammetric Engineering and Remote Sensing,1991,57(3):295-303.
[17] 王华斌,李国元,张本奎,等.资源三号卫星影像融合算法对比分析[J].测绘科学,2015,40(1):47-51. Wang H B,Li G Y,Zhang B K,et al.Contrast and analysis of di-fferent fusion algorithms for ZY-3 satellite images[J].Science of Surveying and Mapping,2015,40(1):47-51.
[18] 黄鹤,冯毅,张萌,等.天绘一号卫星影像的融合及评价研究[J].测绘通报,2013(1):6-9. Huang H,Feng Y,Zhang M,et al.Research on fusion of mapping satellite-1 imagery and its evaluation[J].Bulletin of Surveying and Mapping,2013(1):6-9.
[19] Padwick C,Deskevich M,Pacifici F,et al.WorldView-2 pan-sharpening[C]//American Society for Photogrammetry and Remote Sensing Annual Conference.San Diego:[s.n.],2010.
[20] Klonus S,Ehlers M.Performance of evaluation methods in image fusion[C]//Proceedings of the 12th International Conference on Information Fusion.Seattle,WA:IEEE,2009:1409-1416.
[21] 李弼程,魏俊,彭天强.遥感影象融合效果的客观分析与评价[J].计算机工程与科学,2004,26(1):42-46. Li B C,Wei S,Peng T Q.Objective analysis and evaluation of remote sensing image fusion effect[J].Computer Engineering and Science,2004,26(1):42-46.
[22] 王海晖,彭嘉雄,吴巍,等.多源遥感图像融合效果评价方法研究[J].计算机工程与应用,2003(25):33-37. Wang H H,Peng J X,Wu W,et al.A study of evaluation methods on performance of the multi-source remote sensing image fusion[J].Computer Engineering and Applications,2003(25):33-37.
[23] 张宁玉,吴泉源.Brovey融合与小波融合对QuickBird图像的信息量影响[J].遥感技术与应用,2006,21(1):67-70. Zhang N Y,Wu Q Y.Information influence on QuickBird images by Brovey fusion and wavelet fusion[J].Remote Sensing Technology and Application,2006,21(1):67-70.
[24] Nikolakopoulos K G.Comparison of nine fusion techniques for very high resolution data[J].Photogrammetric Engineering and Remote Sensing,2008,74(5):647-659.
[25] 李俊杰,李杏朝,傅俏燕,等.CBERS-02B星HR与多光谱影像融合及评价[J].国土资源遥感,2008,20(2):43-47.doi:10.6046/gtzyyg.2008.02.11. Li J J,Li X C,Fu Q Y,et al.Fusion and evaluation of CBERS-02B HR and multi-spectral images[J].Remote Sensing for Land and Resources,2008,20(2):43-47.doi:10.6046/gtzyyg.2008.02.11.
[26] 凌静,徐立中,石爱业,等.一种基于Choquet模糊积分小波系数选择的遥感图像融合方法[J]. 遥感学报,2009,13(2):263-268. Ling J,Xu L Z,Shi A Y,et al.Remote sensing images fusion based on wavelet coefficients selection using Choquet fuzzy integral[J].Journal of Remote Sensing,2009,13(2):263-268.
[27] 王建梅,李德仁.QuickBird全色与多光谱数据融合方法用于土地覆盖分类中的比较研究[J].测绘通报,2005(10):37-40,43. Wang J M,Li D R.A study of fusion algorithms of QuickBird pan and multispectral images for land cover classification[J].Bulletin of Surveying and Mapping,2005(10):37-40,43.
[28] Amarsaikhan D,Saandar M,Ganzorig M,et al.Comparison of multisource image fusion methods and land cover classification[J].International Journal of Remote Sensing,2012,33(8):2532-2550.
[29] Richards J A.Remote Sensing Digital Image Analysis[M].Berlin:Springer,1999.
[30] Chikr El-Mezouar M,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.
[31] Pajares G,De la Cruz J M.A wavelet-based image fusion tutorial[J].Pattern Recognition,2004,37(9):1855-1872.
[32] Ghosh A,Joshi P K.Assessment of pan-sharpened very high-resolution WorldView-2 images[J].International Journal of Remote Sensing,2013,34(23):8336-8359.

[1]	白俊龙, 王章琼, 闫海涛. K-means聚类引导的无人机遥感图像阈值分类方法[J]. 自然资源遥感, 2021, 33(3): 114-120.
[2]	苏龙飞, 李振轩, 高飞, 余敏. 遥感影像水体提取研究综述[J]. 国土资源遥感, 2021, 33(1): 9-11.
[3]	黄巍, 黄辉先, 徐建闽, 刘嘉婷. 基于Canny边缘检测思想的改进遥感影像道路提取方法[J]. 国土资源遥感, 2019, 31(1): 65-70.
[4]	贾祎琳, 张文, 孟令奎. 面向GF-1影像的NDWI分割阈值选取方法研究[J]. 国土资源遥感, 2019, 31(1): 95-100.
[5]	郭拯危, 王乐, 宋国磊. 基于混合模糊的SAR图像水陆分割算法[J]. 国土资源遥感, 2018, 30(4): 62-67.
[6]	王学成, 杨飞, 高星, 张英慧. 基于阈值比值法的森林冰雪冻害遥感评估——以湖南省为例[J]. 国土资源遥感, 2018, 30(3): 196-203.
[7]	刘尚旺, 郜刘阳, 王博. 联合双边滤波器和小波阈值收缩去噪算法研究[J]. 国土资源遥感, 2018, 30(2): 114-124.
[8]	李峰, 梁汉东, 赵小平, 白江伟, 崔玉坤. 内蒙古乌达煤田煤火治理效果的遥感监测与评估[J]. 国土资源遥感, 2017, 29(3): 217-223.
[9]	韩丽蓉. 掩模图像生成时阈值取值的合理性探讨[J]. 国土资源遥感, 2017, 29(2): 22-28.
[10]	杨青山, 张华. 融合像元形状和光谱信息的高分遥感图像分类新方法[J]. 国土资源遥感, 2016, 28(4): 64-70.
[11]	黄维, 黄进良, 王立辉, 胡砚霞, 韩鹏鹏. 基于PCA的变化向量分析法遥感影像变化检测[J]. 国土资源遥感, 2016, 28(1): 22-27.
[12]	张倩. 基于双重离散小波变换的遥感图像去噪算法[J]. 国土资源遥感, 2015, 27(4): 14-20.
[13]	李向辉, 陈一祥, 王海斌, 张恩兵, 秦昆. 基于Gabor滤波和局部特征点密度的居民区提取[J]. 国土资源遥感, 2015, 27(3): 59-64.
[14]	李华胜, 黄平平, 苏莹. 一种提取遥感影像中道路信息的方法[J]. 国土资源遥感, 2015, 27(2): 56-62.
[15]	阙昊懿, 黄辉先, 徐建闽. 基于双阈值SSDA模板匹配的遥感图像道路边缘检测研究[J]. 国土资源遥感, 2014, 26(4): 29-33.

Viewed

Full text

Abstract

Cited

Shared

Discussed