遥感影像监督分割评价指标比较与分析
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摘要
遥感影像空间分辨率的不断提高,一方面为使用者提供了更加丰富的地物信息,另一方面却也加大了信息准确高效提取的难度。影像分割是遥感影像目标提取的关键步骤,影像分割的效果直接影响信息提取的精度和准度。面对众多分割算法,影像分割效果评价成为遥感信息提取和目标识别研究的重点之一。面向典型目标识别问题,本文针对遥感影像监督分割评价问题,从实验的角度讨论其中具有代表性的面积匹配指数、相似尺寸指标、相关区域指标、质量合格率、欧氏距离指标1、欧氏距离指标2、面积差异指数和距离指标的实际性能与适用情况。首先,通过一系列实验测算不同分割方法下的影像与参考影像的差异情况,讨论测算结果并评估差异指标的优缺点;然后,通过对比分析与加权计算,提出了遥感影像监督分割综合评价方法,实验表明该方法在一定程度上有助于分割方法的科学选择以及影像信息提取效率的提高;最后,从评价指标与分割方法2个角度系统分析了实验结果,并指出了影像监督分割评价存在的问题以及发展趋势。
The improving spatial resolution of remote sensing imagery provides more abundant information for users,but also increases the difficulty of accurate and efficient extraction of information. Image segmentation is a fundamental step in target extraction from remote sensing imagery. The quality of image segmentation directly affects the accuracy of information extraction from high spatial resolution remote sensing imagery. With various segmentation algorithms,image segmentation evaluation has become one of the research focuses in remote sensing information extraction and target recognition. Aiming at the issue of typical target recognition and from an experimental perspective, this paper compared and analyzed in detail eight representative supervised segmentation indexes: Area Fitness Index(AFI), Similarity of Size(Sim Size), Relative Area in Sub-Object(RAsub), Quality Rate(QR), Euclidian Distance 1(ED1), Euclidian Distance 2(ED2), area discrepancy index(ADI) and Distance-Based Measure(D). Firstly, we employed a series of experiments to calculate the difference between segmentation image and reference image by using different segmentation methods, then discussed the calculation results and evaluated the advantages and disadvantages of the different supervised segmentation evaluation indexes. The comparison results show that the AFI, ED1, ED2 and D could representatively and synthetically assess the segmentation quality. Further, based on the indexes analysis result, this paper proposed a comprehensive evaluation scheme for remote sensing imagery supervised segmentation evaluation by using weighted calculation of the four representative indexes. Through the experiment of comprehensive evaluation, we conclude that the effect of simple shape objects(such as the baseball field and oil tank) by using various segmentation methods is generally ideal. When the shape of objects is complex and the contour is blurred, the accuracy of image segmentation will be sensitive to the segmentation result to some extent. Meanwhile,the effect of segmentation methods(e.g., Otsu-2 D, Regional Growth, and Mean Shift) are in general better than the other segmentation methods(e.g., split and merge, maximal entropy, and fuzzy threshold). In addition, the experiments also suggest that the comprehensive method is helpful for the scientific selection of segmentation methods and it can improve the efficiency of information extraction from high spatial resolution imagery. Finally,this paper systematically analyzed the experimental results from the two aspects of evaluation index and segmentation method, and pointed out the existing problems and development trends of image supervised segmentation evaluation.
The improving spatial resolution of remote sensing imagery provides more abundant information for users,but also increases the difficulty of accurate and efficient extraction of information. Image segmentation is a fundamental step in target extraction from remote sensing imagery. The quality of image segmentation directly affects the accuracy of information extraction from high spatial resolution remote sensing imagery. With various segmentation algorithms,image segmentation evaluation has become one of the research focuses in remote sensing information extraction and target recognition. Aiming at the issue of typical target recognition and from an experimental perspective, this paper compared and analyzed in detail eight representative supervised segmentation indexes: Area Fitness Index(AFI), Similarity of Size(Sim Size), Relative Area in Sub-Object(RAsub), Quality Rate(QR), Euclidian Distance 1(ED1), Euclidian Distance 2(ED2), area discrepancy index(ADI) and Distance-Based Measure(D). Firstly, we employed a series of experiments to calculate the difference between segmentation image and reference image by using different segmentation methods, then discussed the calculation results and evaluated the advantages and disadvantages of the different supervised segmentation evaluation indexes. The comparison results show that the AFI, ED1, ED2 and D could representatively and synthetically assess the segmentation quality. Further, based on the indexes analysis result, this paper proposed a comprehensive evaluation scheme for remote sensing imagery supervised segmentation evaluation by using weighted calculation of the four representative indexes. Through the experiment of comprehensive evaluation, we conclude that the effect of simple shape objects(such as the baseball field and oil tank) by using various segmentation methods is generally ideal. When the shape of objects is complex and the contour is blurred, the accuracy of image segmentation will be sensitive to the segmentation result to some extent. Meanwhile,the effect of segmentation methods(e.g., Otsu-2 D, Regional Growth, and Mean Shift) are in general better than the other segmentation methods(e.g., split and merge, maximal entropy, and fuzzy threshold). In addition, the experiments also suggest that the comprehensive method is helpful for the scientific selection of segmentation methods and it can improve the efficiency of information extraction from high spatial resolution imagery. Finally,this paper systematically analyzed the experimental results from the two aspects of evaluation index and segmentation method, and pointed out the existing problems and development trends of image supervised segmentation evaluation.
引文
[1]明冬萍,骆剑承,沈占锋,等.高分辨率遥感影像信息提取与目标识别技术研究[J].测绘科学,2005,30(3):18-20.[Ming D P, Luo J C, Shen Z F, et al. Research on information extraction and target recognition technology of high resolution remote sensing image[J]. Surveying and mapping science, 2005,30(3):18-20.]
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[3] Blaschke T, Hay G J, Kelly M, et al. Geographic objectbased image analysis-towards a new paradigm[J]. ISPRS Journal of Photogrammetry&Remote Sensing, 2014,87(100):180-191.
[4]张仙,明冬萍.面向地学应用的遥感影像分割评价[J].测绘学报,2015(S1):108-116.[Zhang X, Ming D P. Geo-application oriented evaluations of remote sensing image segmentation[J]. Acta Geodaetica et Cartographica Sinica. 2015(S1):108-116.]
[5] Hoover A, Jean-Baptiste G, Jiang X, et al. An experimental comparison of range image segmentation algorithms[J]. IEEE Transactions on Pattern Analysis&Machine Intelligence, 1996,18(7):673-689.
[6]陈扬洋,明冬萍,徐录,等.高空间分辨率遥感影像分割定量实验评价方法综述[J].地球信息科学学报,2017,19(6):818-830.[Chen Y Y, Ming D P, Xu L, et al. An overview of quantitative experimental methods for segmentation evaluation of high spatial remote sensing images[J]. Journal of Geo-information Science, 2017,19(6):818-830.]
[7] Zhang H, Fritts J E, Goldman S A. Image segmentation evaluation:A survey of unsupervised methods[J]. Computer Vision&Image Understanding, 2008,110(2):260-280.
[8] Chen Y Y, Ming D P, et al. Review on high spatial resolution remote sensing image segmentation evaluation[J].Photogrammetric Engineering&Remote Sensing, 2018,84(10):629-646.
[9]吴波,林珊珊,周桂军.面向对象的高分辨率遥感影像分割分类评价指标[J].地球信息科学学报,2013,15(4):567-573.[Wu B, Lin S S, Zhou G J. Quantitatively evaluating indexes for object-based segmentation of high spatial resolution image[J]. Journal of Geo-information Science,2013,15(4):567-573.]
[10] Anders N S, Seijmonsbergen A C, Bouten W. Segmentation optimization and stratified object-based analysis for semi-automated geomorphological mapping[J]. Remote Sensing of Environment, 2011,115(12):2976-2985.
[11]韦兴旺,张雪锋,薛云.基于光谱和形状的遥感图像分割质量评估方法[J].地球信息科学学报,2018,20(10):1489-1499.[Wei X W, Zhang X F, Xue Y. Remote sensing image segmentation quality assessment based on spectrum and shape[J]. Journal of Geo-information Science, 2018,20(10):1489-1499.]
[12] Lucieer A, Stein A. Existential uncertainty of spatial objects segmented from satellite sensor imagery[J]. IEEE Transactions on Geoscience&Remote Sensing, 2002,40(11):2518-2521.
[13] Zhan Q M, Molenaar M, Tempfli K, et al. Quality assessment for geo-spatial objects derived from remotely sensed data[J]. International Journal of Remote Sensing,2007,26(14):2953-2974.
[14] M?ller M, Lymburner L, Volk M. The comparison index:A tool for assessing the accuracy of image segmentation[J]. International Journal of Applied Earth Observation&Geoinformation, 2007,9(3):311-321.
[15] Weidner U. Contribution to the assessment of segmentation quality for remote sensing applications[C]. Proceedings of the 21stCongress for the International Society for Photogrametry and Remote sensing, 2008.
[16] Clinton N, Holt A, Scarborough J, et al. Accuracy assessment measures for object-based image segmentation goodness[J]. Photogrammetric Engineering&Remote Sensing, 2010,76(3):289-299.
[17] Liu Y, Bian L, Meng Y, et al. Discrepancy measures for selecting optimal combination of parameter values in object-based image analysis[J]. ISPRS Journal of Photogrammetry&Remote Sensing, 2012,68(1):144-156.
[18] Cheng J, Bo Y, Zhu Y, et al. A novel method for assessing the segmentation quality of high-spatial resolution remote-sensing images[J]. International Journal of Remote Sensing, 2014,35(10):3816-3839.
[19] Hoover A, Jean-Baptiste G, Jiang X, et al. An experimental comparison of range image segmentation algorithms[J]. IEEE Transactions on Pattern Analysis&Machine Intelligence, 1996,18(7):673-689.
[20] McCane B. On the evaluation of image segmentation algorithms[C]. Digital Image Computing:Techniques and Applications, 1997:455-461.
[21] Yang J, He Y, Caspersen J, et al. A discrepancy measure for segmentation evaluation from the perspective of object recognition[J]. ISPRS Journal of Photogrammetry&Remote Sensing, 2015,101:186-192.
[22] Sch?pfer E, Lang S. Object fate analysis-A virtual overlay method for the categorization of object transition and object-based accuracy assessment[C]. International Archives of Photogrammetry Remote Sensing&Spatial Information Sciences, 2012.
[23]刘建华,毛政元.高空间分辨率遥感影像分割方法研究综述[J].遥感信息,2009(6):95-101.[Liu J H, Mao Z Y.Summary of high spatial resolution remote sensing image segmentation methods[J]. Remote sensing information,2009(6):95-101.]
[24]陈忠,赵忠明.基于区域生长的多尺度遥感图像分割算法[J].计算机工程与应用,2005,41(35):7-9.[Chen Z, Zhao Z M. A multi-scale remote sensing image segmentation algorithm based on region growing[J]. Computer Engineering and Applications, 2005,41(35):7-9.]
[25]刘大伟.高分辨率遥感影像分割方法及应用研究[D].西安:长安大学,2016.[Liu D W. Study on Segmentation Algorithms and its Applications of high spatial resolution remote sensing images[D]. Xi'an:Chang'an University, 2016.]
[2] Blaschke T, Strobl J. What’s wrong with pixels? Some recent developments interfacing remote sensing and GIS[J].GeoBIT/GIS, 2001,14:12-17.
[3] Blaschke T, Hay G J, Kelly M, et al. Geographic objectbased image analysis-towards a new paradigm[J]. ISPRS Journal of Photogrammetry&Remote Sensing, 2014,87(100):180-191.
[4]张仙,明冬萍.面向地学应用的遥感影像分割评价[J].测绘学报,2015(S1):108-116.[Zhang X, Ming D P. Geo-application oriented evaluations of remote sensing image segmentation[J]. Acta Geodaetica et Cartographica Sinica. 2015(S1):108-116.]
[5] Hoover A, Jean-Baptiste G, Jiang X, et al. An experimental comparison of range image segmentation algorithms[J]. IEEE Transactions on Pattern Analysis&Machine Intelligence, 1996,18(7):673-689.
[6]陈扬洋,明冬萍,徐录,等.高空间分辨率遥感影像分割定量实验评价方法综述[J].地球信息科学学报,2017,19(6):818-830.[Chen Y Y, Ming D P, Xu L, et al. An overview of quantitative experimental methods for segmentation evaluation of high spatial remote sensing images[J]. Journal of Geo-information Science, 2017,19(6):818-830.]
[7] Zhang H, Fritts J E, Goldman S A. Image segmentation evaluation:A survey of unsupervised methods[J]. Computer Vision&Image Understanding, 2008,110(2):260-280.
[8] Chen Y Y, Ming D P, et al. Review on high spatial resolution remote sensing image segmentation evaluation[J].Photogrammetric Engineering&Remote Sensing, 2018,84(10):629-646.
[9]吴波,林珊珊,周桂军.面向对象的高分辨率遥感影像分割分类评价指标[J].地球信息科学学报,2013,15(4):567-573.[Wu B, Lin S S, Zhou G J. Quantitatively evaluating indexes for object-based segmentation of high spatial resolution image[J]. Journal of Geo-information Science,2013,15(4):567-573.]
[10] Anders N S, Seijmonsbergen A C, Bouten W. Segmentation optimization and stratified object-based analysis for semi-automated geomorphological mapping[J]. Remote Sensing of Environment, 2011,115(12):2976-2985.
[11]韦兴旺,张雪锋,薛云.基于光谱和形状的遥感图像分割质量评估方法[J].地球信息科学学报,2018,20(10):1489-1499.[Wei X W, Zhang X F, Xue Y. Remote sensing image segmentation quality assessment based on spectrum and shape[J]. Journal of Geo-information Science, 2018,20(10):1489-1499.]
[12] Lucieer A, Stein A. Existential uncertainty of spatial objects segmented from satellite sensor imagery[J]. IEEE Transactions on Geoscience&Remote Sensing, 2002,40(11):2518-2521.
[13] Zhan Q M, Molenaar M, Tempfli K, et al. Quality assessment for geo-spatial objects derived from remotely sensed data[J]. International Journal of Remote Sensing,2007,26(14):2953-2974.
[14] M?ller M, Lymburner L, Volk M. The comparison index:A tool for assessing the accuracy of image segmentation[J]. International Journal of Applied Earth Observation&Geoinformation, 2007,9(3):311-321.
[15] Weidner U. Contribution to the assessment of segmentation quality for remote sensing applications[C]. Proceedings of the 21stCongress for the International Society for Photogrametry and Remote sensing, 2008.
[16] Clinton N, Holt A, Scarborough J, et al. Accuracy assessment measures for object-based image segmentation goodness[J]. Photogrammetric Engineering&Remote Sensing, 2010,76(3):289-299.
[17] Liu Y, Bian L, Meng Y, et al. Discrepancy measures for selecting optimal combination of parameter values in object-based image analysis[J]. ISPRS Journal of Photogrammetry&Remote Sensing, 2012,68(1):144-156.
[18] Cheng J, Bo Y, Zhu Y, et al. A novel method for assessing the segmentation quality of high-spatial resolution remote-sensing images[J]. International Journal of Remote Sensing, 2014,35(10):3816-3839.
[19] Hoover A, Jean-Baptiste G, Jiang X, et al. An experimental comparison of range image segmentation algorithms[J]. IEEE Transactions on Pattern Analysis&Machine Intelligence, 1996,18(7):673-689.
[20] McCane B. On the evaluation of image segmentation algorithms[C]. Digital Image Computing:Techniques and Applications, 1997:455-461.
[21] Yang J, He Y, Caspersen J, et al. A discrepancy measure for segmentation evaluation from the perspective of object recognition[J]. ISPRS Journal of Photogrammetry&Remote Sensing, 2015,101:186-192.
[22] Sch?pfer E, Lang S. Object fate analysis-A virtual overlay method for the categorization of object transition and object-based accuracy assessment[C]. International Archives of Photogrammetry Remote Sensing&Spatial Information Sciences, 2012.
[23]刘建华,毛政元.高空间分辨率遥感影像分割方法研究综述[J].遥感信息,2009(6):95-101.[Liu J H, Mao Z Y.Summary of high spatial resolution remote sensing image segmentation methods[J]. Remote sensing information,2009(6):95-101.]
[24]陈忠,赵忠明.基于区域生长的多尺度遥感图像分割算法[J].计算机工程与应用,2005,41(35):7-9.[Chen Z, Zhao Z M. A multi-scale remote sensing image segmentation algorithm based on region growing[J]. Computer Engineering and Applications, 2005,41(35):7-9.]
[25]刘大伟.高分辨率遥感影像分割方法及应用研究[D].西安:长安大学,2016.[Liu D W. Study on Segmentation Algorithms and its Applications of high spatial resolution remote sensing images[D]. Xi'an:Chang'an University, 2016.]