利用纹理融合与广义高斯模型的高分辨率SAR影像变化检测
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摘要
为了充分利用高分辨率SAR影像的纹理特征,提出一种纹理信息融合与广义高斯模型相结合的SAR影像变化检测方法。通过灰度共生矩阵计算影像的纹理特征进而构造纹理差异影像,利用离散平稳小波变换,融合灰度差异影像和纹理差异影像。然后利用广义高斯模型进行统计建模,估计融合后差异影像上变化类和未变化类的概率分布,利用KI阈值准则获取最佳分割阈值,实现多时相SAR影像的非监督变化检测。选取两组TerraSAR-X数据进行实验,结果表明融合纹理信息与广义高斯模型的变化检测方法可行,其中融合逆差距纹理信息的检测性能最优。
In order to make full use of high resolution SAR image texture feature,this paper proposes a method for SAR image change detection combined with texture information fusion and generalized Gaussian model.First,obtain image texture feature through calculating of gray level co-occurrence matrix and then construct texture difference image.Second,fuse gray difference image and texture difference image by discrete stationary wavelet transform.Finally,estimate the probability distribution of unchanged pixel and changed pixel in fusion difference image by generalized Gaussian model and achieve unsupervised change detection on multi-temporal SAR images by using KI threshold criterion to obtain the best segmentation threshold.This paper uses two images sets of TerraSAR-X to experiment and the result shows that it is feasible for change detection combined with texture information fusion and the generalized Gaussian model,which can get the best performance by fusing texture feature of inverse difference moment.
In order to make full use of high resolution SAR image texture feature,this paper proposes a method for SAR image change detection combined with texture information fusion and generalized Gaussian model.First,obtain image texture feature through calculating of gray level co-occurrence matrix and then construct texture difference image.Second,fuse gray difference image and texture difference image by discrete stationary wavelet transform.Finally,estimate the probability distribution of unchanged pixel and changed pixel in fusion difference image by generalized Gaussian model and achieve unsupervised change detection on multi-temporal SAR images by using KI threshold criterion to obtain the best segmentation threshold.This paper uses two images sets of TerraSAR-X to experiment and the result shows that it is feasible for change detection combined with texture information fusion and the generalized Gaussian model,which can get the best performance by fusing texture feature of inverse difference moment.
引文
[1]ZHAO J,YANG J,LU Z,et al.A Novel Method of Change Detection in Bi-Temporal PolSAR Data Using a Joint-Classification Classifier Based on a Similarity Measure[J].Remote Sensing,2017,9(8):846.
[2]刘莹,陶超,闫培,等.图割能量驱动的高分辨率遥感影像震害损毁建筑物检测[J].测绘学报,2017,46(7):910-917.
[3]杨育丽,马明国,周兆叶.基于地形梯度的山地流域土地利用格局及时空演变研究-以云南小江流域为例[J].测绘工程,2017,26(12):8-16.
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[8]SUN W,SHI L,YANG J,et al.BuildingCollapse Assessment in Urban Areas Using Texture Information from Postevent SAR Data[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2016,9(8):3792-3808.
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[11]李坤,杨然,王雷光,等.基于敏感特征向量的SAR图像灾害变化检测技术[J].计算机工程与应用,2012,48(3):24-28.
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[13]赵凌君,秦玉亮,高贵,等.利用GLCM纹理分析的高分辨率SAR图像建筑区检测[J].遥感学报,2009(3):475-490.
[14]BAZI Y,BRUZZONE L,MELGANI F.An Unsupervised Approach Based on The Generalized Gaussian Model to Automatic Change Detection in Multitemporal SAR Images[J].IEEE Transactions on Geoscience and Remote Sensing,2005,43(4):874-887.
[15]袁增云,胡召玲.基于广义高斯模型的多时相SAR图像变化检测[J].江苏师范大学学报(自然科学版),2013,31(1):74-78.
[16]胡召玲.广义高斯模型及KI双阈值法的SAR图像非监督变化检测[J].测绘学报,2013(1):116-122.
[17]ZHAO J Q,YANG J,LI P X,et al.An Unsupervised Change Detection Based on Test Statistic and KI from Multi-temporal and Full Polarimetric SAR Images[J].ISPRS-International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences,2016:611-615.
[18]ZHAO J Q,YANG J,LU Z,et al.An Unsupervised Method of Change Detection in Multi-Temporal PolSAR Data Using a Test Statistic and An Improved K&I Algorithm[J].Applied Sciences,2017,7(12):1297.
[19]HARALICK R M,SHANMUGAM K.Textural Features for Image Classification[J].IEEE Transactions on systems,man,and cybernetics,1973(6):610-621.
[20]GONG M G,ZHOU Z Q,MA J J.Change Detection in Synthetic Aperture Radar Images Based on Image Fusion and Fuzzy Clustering[J].IEEE Transactions on Image Processing,2012,21(4):2141-2151.
[21]慕彩红,霍利利,刘逸,等.基于小波融合和PCA-核模糊聚类的遥感图像变化检测[J].电子学报,2015,43(7):1375-1381.
[2]刘莹,陶超,闫培,等.图割能量驱动的高分辨率遥感影像震害损毁建筑物检测[J].测绘学报,2017,46(7):910-917.
[3]杨育丽,马明国,周兆叶.基于地形梯度的山地流域土地利用格局及时空演变研究-以云南小江流域为例[J].测绘工程,2017,26(12):8-16.
[4]SCHLAFFER S,MATGEN P,HOLLAUS M,et al.Flood Detection from Multi-temporal SAR Data Using Harmonic Analysis and Change Detection[J].International Journal of Applied Earth Observation and Geoinformation,2015(38):15-24.
[5]LEFEBVRE A,CORPETTI T,HUBERT-MOY L.Object-oriented Approach and Texture Analysis for Change Detection in Very High Resolution Images[C]//Geoscience and Remote Sensing Symposium,2008,IV-663-IV-666.
[6]TISON C,NICOLAS J M,TUPIN F,et al.A New Statistical Model for Markovian Classification of Urban Areas in High-resolution SAR Images[J].IEEE Transactions on Geoscience and Remote Sensing,2004,42(10):2046-2057.
[7]WANG G G,HUANG X T,ZHOU Z M,et al.A New SAR Image Change Detection Algorithm Based on Texture Feature[C]//Synthetic Aperture Radar(APSAR),2011 3rd International Asia-Pacific Conference on.IEEE,2011:1-4.
[8]SUN W,SHI L,YANG J,et al.BuildingCollapse Assessment in Urban Areas Using Texture Information from Postevent SAR Data[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2016,9(8):3792-3808.
[9]SHI L,SUN W,YANG J,et al.Building Collapse Assessment by the Use of Postearthquake Chinese VHR Airborne SAR[J].IEEE Geoscience and Remote Sensing Letters,2015,12(10):2021-2025.
[10]陈志鹏,邓鹏,种劲松,等.纹理特征在SAR图像变化检测中的应用[J].遥感技术与应用,2002,17(3):162-166.
[11]李坤,杨然,王雷光,等.基于敏感特征向量的SAR图像灾害变化检测技术[J].计算机工程与应用,2012,48(3):24-28.
[12]WANG G,HUANG X,ZHOU Z,et al.A New SAR Image Change Detection Algorithm Based on Texture Feature[C]//Synthetic Aperture Radar(APSAR),2011 3rd International Asia-Pacific Conference on.IEEE,2011:1-4.
[13]赵凌君,秦玉亮,高贵,等.利用GLCM纹理分析的高分辨率SAR图像建筑区检测[J].遥感学报,2009(3):475-490.
[14]BAZI Y,BRUZZONE L,MELGANI F.An Unsupervised Approach Based on The Generalized Gaussian Model to Automatic Change Detection in Multitemporal SAR Images[J].IEEE Transactions on Geoscience and Remote Sensing,2005,43(4):874-887.
[15]袁增云,胡召玲.基于广义高斯模型的多时相SAR图像变化检测[J].江苏师范大学学报(自然科学版),2013,31(1):74-78.
[16]胡召玲.广义高斯模型及KI双阈值法的SAR图像非监督变化检测[J].测绘学报,2013(1):116-122.
[17]ZHAO J Q,YANG J,LI P X,et al.An Unsupervised Change Detection Based on Test Statistic and KI from Multi-temporal and Full Polarimetric SAR Images[J].ISPRS-International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences,2016:611-615.
[18]ZHAO J Q,YANG J,LU Z,et al.An Unsupervised Method of Change Detection in Multi-Temporal PolSAR Data Using a Test Statistic and An Improved K&I Algorithm[J].Applied Sciences,2017,7(12):1297.
[19]HARALICK R M,SHANMUGAM K.Textural Features for Image Classification[J].IEEE Transactions on systems,man,and cybernetics,1973(6):610-621.
[20]GONG M G,ZHOU Z Q,MA J J.Change Detection in Synthetic Aperture Radar Images Based on Image Fusion and Fuzzy Clustering[J].IEEE Transactions on Image Processing,2012,21(4):2141-2151.
[21]慕彩红,霍利利,刘逸,等.基于小波融合和PCA-核模糊聚类的遥感图像变化检测[J].电子学报,2015,43(7):1375-1381.