面向林地分类的GF-2影像融合算法评价
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摘要
针对林业部门目前常用的遥感影像融合算法,探究适合于林地信息提取的GF-2影像融合算法。以GF-2卫星1 m全色/4 m多光谱分辨率平面影像为数据源,采用HSV变换(hue-saturation-value,颜色空间变换),Brovey变换(彩色标准化变换),PC变换(principle components,主成分变换),HPF变换(high-pass fusion,高通滤波变换),GS变换(gram-schmidt,正交化变换),Pansharp变换(超分辨率贝叶斯变换)等6种常用融合算法,通过目视和定量特征分析对融合效果进行评价,并结合面向对象分类方法对融合后影像进行地类信息提取和分析,探讨6种融合算法对GF-2影像在林区地类提取的适宜性。研究结果表明:基于Brovey和HSV算法的融合结果目视效果良好,清晰度与纹理增强明显;这2种融合算法影像在不同地类信息的提取上各有优势,HSV算法融合结果在不同地类的提取上效果最好,分类总精度可达85.14%,Brovey算法融合结果则在森林类型的提取上具有最高的分类总精度,为75.72%;其余4种融合算法在图像质量及其他地类提取中各有优势,具体融合算法的选取需根据应用目的和影区应用区域的实际情况而定。该研究可为林业部门提高GF-2卫星的适用性及大规模应用提供参考。
To obtain an optimal method for image enhancement of GF-2 forestry area data, six frequently-used methods were analyzed: Brovey transformation; hue, saturation, and value(HSV) transformation; Principle Component(PC) spectral sharpening; high pass filter(HPF) spectral sharpening; Gram-Schmidt spectral sharpening; and Pansharp transformation. Qualitative and quantitative analyses were used to assess the effect and quality of the fusion images. Indexes include mean, average gradient, high-frequency information integration, correlation index, entropy index and second moment index. Among them, correlation index and second moment index were calculate by ENVI, other indexes were all by Matlab. Furthermore, to access an appropriate fusion method for GF-2 forestland data extraction, fusion images were classified by performance of fusion methods at two information extraction levels based on an object-oriented classification method. All the transformations used the same parameter and methods on each level, and use the same samples to classify and accuracy check. Results showed that correlation index and high-frequency information integration of HSV transformation could reach 0.823 and 0.570, respectively. In addition, the entropy index and second moment index could improved 25% and 50% compared to original multiple image, respectively. It had a better visual effect with obvious enhanced clarity and texture features. For classification experiments, HSV and Brovey transformations had their own superiority for the extraction of different classes with the HSV transformation having the highest overall classification accuracy of 85.1% and the Brovey transformation having the highest accuracy on the second level of 75.7%. The other four methods had different advantages for quality and information extraction of the fusion images. Thus, the final selection of fusion methods should consider practical forestry application and image information which could provide a reference for GF-2 images to be applied on a large scale in forested areas.
To obtain an optimal method for image enhancement of GF-2 forestry area data, six frequently-used methods were analyzed: Brovey transformation; hue, saturation, and value(HSV) transformation; Principle Component(PC) spectral sharpening; high pass filter(HPF) spectral sharpening; Gram-Schmidt spectral sharpening; and Pansharp transformation. Qualitative and quantitative analyses were used to assess the effect and quality of the fusion images. Indexes include mean, average gradient, high-frequency information integration, correlation index, entropy index and second moment index. Among them, correlation index and second moment index were calculate by ENVI, other indexes were all by Matlab. Furthermore, to access an appropriate fusion method for GF-2 forestland data extraction, fusion images were classified by performance of fusion methods at two information extraction levels based on an object-oriented classification method. All the transformations used the same parameter and methods on each level, and use the same samples to classify and accuracy check. Results showed that correlation index and high-frequency information integration of HSV transformation could reach 0.823 and 0.570, respectively. In addition, the entropy index and second moment index could improved 25% and 50% compared to original multiple image, respectively. It had a better visual effect with obvious enhanced clarity and texture features. For classification experiments, HSV and Brovey transformations had their own superiority for the extraction of different classes with the HSV transformation having the highest overall classification accuracy of 85.1% and the Brovey transformation having the highest accuracy on the second level of 75.7%. The other four methods had different advantages for quality and information extraction of the fusion images. Thus, the final selection of fusion methods should consider practical forestry application and image information which could provide a reference for GF-2 images to be applied on a large scale in forested areas.
引文
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[19]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.WANG Guangjie,ZHOU Jieming,YANG Cunjian,et al.Analysis of remote sensing image fusion based on different algorithms[J].J Sichuan Norm Univ Nat Sci,2011,34(2):255-259.
[2]赵英时.遥感应用分析原理与方法[M].北京:科学出版社,2003:251-262.
[3]李霖,佘梦媛,罗恒.ZY-3卫星全色与多光谱影像融合方法比较[J].农业工程学报,2014,30(16):157-165.LI Lin,SHE Mengyuan,LUO Heng.Comparison on fusion algorithms of ZY-3 panchromatic and multi-spectral images[J].Trans Chin Soc Agric Eng,2014,30(16):157-165.
[4]杨丽萍,夏敦胜,陈发虎.Landsat 7 ETM+全色与多光谱数据融合算法的比较[J].兰州大学学报(自然科学版),2007,43(4):7-11.YANG Liping,XIA Dunsheng,CHEN Fahu.On fusion algorithms of Landsat 7 ETM+PAN and multi-spectral images[J].J Lanzhou Univ Nat Sci,2007,43(4):7-11.
[5]高志雄.基于ZY-3卫星的森林分类及郁闭度提取方法研究[D].北京:北京林业大学,2015.GAO Zhixiong.Study on the Forest Classification and Crown Density Extraction Method Based on ZY-3 Image Data[D].Beijing:Beijing Forestry University,2015.
[6]郑著彬,李俊,任静丽.HPF图像融合技术在大理市遥感影像中的运用探讨[J].云南地理环境研究,2007,19(6):96-98.ZHENG Zhubin,LI Jun,REN Jingli.Discussion for technology of HPF resolution merge using in Dali’s remote sensing image[J].Yunnan Geogr Environ Res,2007,19(6):96-98.
[7]赵珍梅,马伟,王润生.3种高保真遥感影像融合方法效果评价与分析[J].地址与勘探,2010,46(4):705-710.ZHAO Zhenmei,MA Wei,WANG Runsheng.Evaluation and analysis of there methods of fusion remote sensing images with high fidelity of information[J].Geol Explor,2010,46(4):705-710.
[8]郑中,祁元,张金龙.基于光谱角和光谱距离评价指标的遥感影像融合算法比较研究:以Quick Bird数据为例[J].遥感技术与应用,2013,28(3):437-443.ZHENG Zhong,QI Yuan,ZHANG Jinlong.Comparing with different remote sensing image fusion method based on evaluation index of spectral angle and spectral distance:taking Quickbird datas as example[J].Rem Sens Technol Appl,2013,28(3):437-443.
[9]郭会敏,洪运富,李营,等.基于高分一号卫星影像的多种融合算法比较[J].地理与地理信息科学,2015,31(1):23-26.GUO Huimin,HONG Yunfu,LI Ying,et al.Comparison of fusion methods used for GF-1 satellite image[J].Geogr Geo-Inf Sci,2015,31(1):23-26.
[10]王海江,王周龙,李丽宏,等.基于高平衡阶多进制多小波包变换的遥感影像融合[J].农业工程学报,2015,31(1):178-186.WANG Haijiang,WANG Zhoulong,LI Lihong,et al.Remote sensing image fusion based on high-balanced multiband multiwavelet packet transform[J].Trans Chin Soc Agric Eng,2015,31(1):178-186.
[11]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 Huabin,LI Guoyuan,ZHANG Benkui,et al.Construct and analysis of different fusion algorithms for ZY-3satellite images[J].Sci Surv Mapp,2015,40(1):47-51.
[12]陈春华,苏逸平,邹崇尧.World View-2遥感影像融合算法实验研究[J].地理空间信息,2013,11(6):72-74.CHEN Chunhua,SU Yiping,ZOU Chongyao.Study on remote sensing image fusion algorithm[J].Geospat Inf,2013,11(6):72-74.
[13]许民.高分辨率遥感影像融合方法研究及融合效果评价:以ALOS和SPOT的全色与多光谱影像为例[D].兰州:兰州大学,2010.XU Min.Reseach on High Resolution Remotely Sensed Image Fusion Methods and Evaluation of Fusion Quality:A Case Study of Mutlti-spectral and Panchromatic Data of ALOS and SPOT[D].Lanzhou:Lanzhou University,2010
[14]黄先德,周群,王兴.资源三号卫星全色与多光谱影像融合算法[J].测绘通报,2015(1):109-114.HUANG Xiande,ZHOU Qun,WANG Xing.Fusion of resources satellite-3 remote sensing panchromatic and multispectral Images[J].Bull Surv Mapp,2015(1):109-114.
[15]郑丽.SPOT多光谱影像与全色影像融合研究[J].佳木斯大学学报(自然科学版),2015,33(6):868-871.ZHENG Li.Research on the SPOT multi-spectral image and panchromatic image fusion[J].J Jiamusi Univ Nat Sci Ed,2015,33(6):868-871.
[16]任德智,潘刚,葛丽雯.基于Titan Image软件的Quick Bird影像融合[J].东北林业大学学报,2013,41(3):131-134.REN Dezhi,PAN Gang,GE Liwen.Image fusion of Quick Bird based on Titan Image software[J].2013,41(3):131-134.
[17]王乐,牛雪峰,魏斌,等.遥感影像融合质量评价方法研究[J].测绘通报,2015(2):77-79.WNAG Le,NIU Xuefeng,WEI Bin,et al.Study on quality evaluation methods for remotely sensed images fusion[J].Bull Surv Mapp,2015(2):77-79.
[18]孟京辉,陆元昌,刘刚,等.基于ETM+遥感图像的图像融合试验及评价方法[J].南京林业大学学报(自然科学版),2010,34(1):69-72.MENG Jinghui,LU Yuanchang,LIU Gang,et al.Trail of image fusion methods and quality assessment for ETM+image[J].J Nanjing For Univ Nat Sci Ed,2010,34(1),69-72.
[19]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.WANG Guangjie,ZHOU Jieming,YANG Cunjian,et al.Analysis of remote sensing image fusion based on different algorithms[J].J Sichuan Norm Univ Nat Sci,2011,34(2):255-259.