融入边界特征的遥感影像多尺度分割
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摘要
以往的遥感影像多尺度分割方法对边界特征分析运用较少,为此提出了融入边界特征的多尺度加权聚合遥感影像分割方法(edge-incorporated multi-scale image segmentation by weighted aggregation,EIMSSWA)。首先,检测影像梯度特征生成边界图;然后,在基元合并过程中计算相邻基元间公共边界的多种统计特征,并将其同基元的其他区域特征相结合,优化基元间的相似性度量,提高影像多尺度分割结果的精度;最后,通过e Cognition软件的多尺度分割、基于加权聚合的影像分割(segmentation by weighted aggregation,SWA)和EIMSSWA等3组实验来验证方法的分割精度。结果表明,EIMSSWA方法能够取得更高精度、更合理的影像分割结果。
Some existing remote sensing image segmentation methods do not take the edge feature into consideration,therefore,an edge-incorporated multi-scale segmentation algorithm based on weighted aggregation( EIMSSWA) is proposed. Firstly,the edge features of adjacent primitives are generated by counting the gradient strength and gradient direction on the common edges. Secondly,these features are infused into the similarity measurement of the adjacent primitives in segmentation by weighted aggregation,so as to improve the segmentation.Finally,the segmentation of the proposed method is compared with segmentations of eCognition as well as segmentation by weighted aggregation( SWA) a. The results demonstrate that the EIMSSWA method is capable of gaining more accurate and more reasonable segmentation.
Some existing remote sensing image segmentation methods do not take the edge feature into consideration,therefore,an edge-incorporated multi-scale segmentation algorithm based on weighted aggregation( EIMSSWA) is proposed. Firstly,the edge features of adjacent primitives are generated by counting the gradient strength and gradient direction on the common edges. Secondly,these features are infused into the similarity measurement of the adjacent primitives in segmentation by weighted aggregation,so as to improve the segmentation.Finally,the segmentation of the proposed method is compared with segmentations of eCognition as well as segmentation by weighted aggregation( SWA) a. The results demonstrate that the EIMSSWA method is capable of gaining more accurate and more reasonable segmentation.
引文
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[2]明冬萍．高分辨率遥感特征基元提取与格局判别方法研究[D]．北京:中国科学院研究生院，2006.Ming D P. Study of Parcel Unit Extraction Based-on Features and Pattern Distinguishment from High Spatial Resolution Remote Sensing Images[D]. Beijing:Graduate University of Chinese Academy of Sciences,2006.
[3]骆剑承，周成虎，沈占锋，等．遥感信息图谱计算的理论方法研究[J]．地球信息科学学报，2009,11(5):664-669.Luo J C,Zhou C H,Shen Z F,et al. Theoretic and methodological review on sensor information tupu computation[J]. Journal of GeoInformation Science,2009,11(5):664-669.
[4]陈忠．高分辨率遥感图像分类技术研究[D]．北京:中国科学院研究生院，2006.Chen Z. Research on High Resolution Remote Sensing Image Classification Technology[D]. Beijing:Graduate University of Chinese Academy of Sciences,2006.
[5] Baatz M,Schape A. Multiresolution segmentation:An optimization approach for high quality multi-scale image segmentation[J]. Journal of Photogrammetry and Remote Sensing,2000,58(3-4):12-23.
[6] Huang X,Zhang L P. A comparative study of spatial approaches for urban mapping using hyperspectral ROSIS images over Pavia City,northern Italy[J]. International Journal of Remote Sensing,2009,30(12):3205-3221.
[7] Comaniciu D,Meer P. Mean shift:A robust approach toward feature space analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(5):603-619.
[8]明冬萍，周文，汪闽．基于谱空间统计特征的高分辨率影像分割尺度估计[J]．地球信息科学学报，2016,18(5):622-631.Ming D P,Zhou W,Wang M. Scale parameter estimation based on the spatial and spectral statistics in high spatial resolution image segmentation[J]. Journal of Geo-Information Science,2016,18(5):622-631.
[9] Sharon E,Brandt A,Basri R. Fast multiscale image segmentation[C]//Proceedings IEEE Conference on Computer Vision and Pattern Recognition. IEEE,2000:70-77.
[10] Liu Y F,Zou L,Li J,et al. Segmentation by weighted aggregation and perceptual hash for pedestrian detection[J]. Journal of Visual Communication and Image Representation,2016,36:80-89.
[11] Boykov Y,Jolly M P. Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images[C]//International Conference on Computer Vision,IEEE Computer Society.IEEE,2001:105-112.
[12] Wang X Y,Sun W W,Wu Z F,et al. Color image segmentation using PDTDFB domain hidden Markov tree model[J]. Applied Soft Computing Journal,2015,29:138-152.
[13] Choi H,Baraniuk R G. Multiscale image segmentation using wavelet-domain hidden Markov models[J]. IEEE Transactions on Image Processing,2001,10(9):1309-1321.
[14] Duda R O,Hart P E. Use of the hough transformation to detect lines and curves in pictures[J]. Communications of the ACM,1972,15(1):11-15.
[15] Sharon E,Galum M,Sharon D,et al. Hierarchy and adaptivity in segmenting visual scenes[J]. Nature,2006,442(7104):810-813.
[16]贾建华，焦李成．空间一致性约束谱聚类算法用于图像分割[J]．红外与毫米波学报，2010,29(1):69-74.Jia J H,Jiao L C. Image segmentation by spectral clustering algorithm with spatial coherence constraints[J]. Journal of Infrared and Millimeter Waves,2010,29(1):69-74.
[17] Shi J B,Malik J. Normalized cuts and image segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(8):888-905.
[18]王爱萍，王树根，吴会证．利用分层聚合进行高分辨率遥感影像多尺度分割[J]．武汉大学学报(信息科学版)，2009,34(9):1055-1058.Wang A P,Wang S G,Wu H Z. Multiscale segmentation of high resolution satellite imagery by hierarchical aggregation[J]. Geomatics and Information Science of Wuhan University,2009,34(9):1055-1058.
[19]章毓晋．图像工程[M]. 2版．北京:清华大学出版社，2007.Zhang Y J. Image Engineering[M]. 2nd ed. Beijing:Tsinghua University Press,2007.
[20] Battz M,Bentz U,Dehghanni S et al. e Cognition Professional User Guide 8[M]. Munchen:Definiens AG,2009.
[21]明冬萍，骆剑承，周成虎，等．高分辨率遥感影像特征分割及算法评价分析[J]．地球信息科学，2006,8(1):103-109.Ming D P,Luo J C,Zhou C H,et al. Research on high resolution remote sensing image segmentation methods based on features and evaluation of algorithms[J]. Geo-Information Science,2006,8(1):103-109.
[22]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，2015,44(s1):108-116.Zhang X,Ming D P. Geo-application oriented evaluations of remote sensing image segmentation[J]. Acta Geodaetica et Cartographica Sinica,2015,44(s1):108-116.
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