DSSRM级联分割的SAR图像变化检测
SAR image change detection method of DSSRM based on cascade segmentation
- 2017年21卷第4期 页码:614-621
纸质出版日期: 2017-7 ,
录用日期: 2017-3-3
DOI: 10.11834/jrs.20176330
扫 描 看 全 文
浏览全部资源
扫码关注微信
纸质出版日期: 2017-7 ,
录用日期: 2017-3-3
扫 描 看 全 文
张建龙, 王斌. 2017. DSSRM级联分割的SAR图像变化检测. 遥感学报, 21(4): 614–621
Zhang J L and Wang B. 2017. SAR image change detection method of DSSRM based on cascade segmentation. Journal of Remote Sensing, 21(4): 614–621
SRM(Statistical Region Merging)分割算法具有快速、稳定和抗噪强的优点,基于此,本文提出一种基于DSSRM(Dynamic Sorting Statistical Region Merging)级联分割的SAR图像变化检测方法。首先,针对SRM算法基于单特征静态排序导致的过分割问题,提出一种动态排序模式的DSSRM算法以减少差异图像分割错误,该算法建立基于合并区域的多特征马氏距离排序准则,在每次合并之后更新区域邻接矩阵并重新排序;然后,基于互信息最小化准则构造多通道差异数据集以提高算法对区域合并的约束能力;最后,提出一种级联分割变化检测框架,第1级利用SRM算法将差异图像映射到超像素空间,第2级采用DSSRM算法对超像素进行动态合并获得收敛的分割结果,第3级采用简化SRM方法进行三次合并获得最终的变化检测图。实验结果表明,该方法可以获得比SRM方法和目前流行方法更好的检测性能。
Synthetic Aperture Radar (SAR) images have region homogeneity with gray and texture. Considering that SRM (Statistical Region Merging) algorithms of image segmentation are efficient
stable and robust against noise
we propose a novel change detection method based on cascade segmentation with Dynamic Sorting Statistical Region Merging (DSSRM) algorithm. Firstly a DSSRM algorithm based on dynamic sorting is proposed to overcome conventional SRM's over-segmentation problem caused by single feature and static sorting. This algorithm takes the Manhattan distance of multi-feature of regions to be merged as the sorting criterion
and updates the adjacency matrix after each merging. Secondly based on the rule of minimizing mutual information we design a multi-channel complementary appearance model to improve the capability of constraint for region merging. Finally we present a cascade change detection framework with multiple levels. The first level projects difference image to super-pixel space via SRM
the second level utilizes DSSRM to dynamically merge regions; and the third level leverages a simplified SRM to realize region merging again to obtain final change detection map. Experimental results of the proposed method and proposed methods based on PCA and MRF are presented. By analysis and quantitative comparisons
the false alarm number and total of error number by DSSRMare decreased thereby the performance of KAPPA can get higher than methods based on PCA and MRF. DSSRM method is based on dynamic sorting algorithm with Manhattan distance of multi-feature of regions
it makes similar regions to be merged firstly. Experiments on construction of multi-channel illustrates that the more is the difference between channels the better is the performance of change detection. Our method improved the performance of SRM algorithm to avoid the over-segmentation phenomenon. Comparison experiments show that this method can obtain better performance of change detection than conventional SRM and state of art algorithms.
合成孔径雷达图像变化检测动态排序统计区域合并
SAR imagechange detectionMDSSRMcascade segmentation
Bovolo F and Bruzzone L. 2007. A theoretical framework for unsupervised change detection based on change vector analysis in the polar domain. IEEE Transactions on GeoscienceandRemote Sensing, 45(1): 218–236
Celik T. 2009. Unsupervised change detection in satellite images using principal component analysis and k-means clustering. IEEE Geoscience and Remote Sensing Letters, 6(4): 772–776
Coppin P, Jonckheere I, Nackaerts K, Muys B and Lambin E. 2004. Digital change detection methods in ecosystem monitoring: a review. International Journal of Remote Sensing, 25(9): 1565–1596
佃袁勇, 方圣辉, 姚崇怀. 2016. 多尺度分割的高分辨率遥感影像变化检测. 遥感学报, 20(1): 129–137
Dian Y Y, Fang S H and Yao C H. 2016. Change detection for high-resolution images using multilevel segment method. Journal of Remote Sensing, 20(1): 129–137
Feng J, Cao Z and Pi Y. 2013. Multiphase SAR image segmentation with G0-statistical-model-based activecontours. IEEE Transactions on Geoscience and Remote Sensing, 51(7): 4190–4199
Gao X B, Wang B, Tao D C and Li X L. 2011. A relay level set method for automatic image segmentation. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 41(2): 518–525
Gong M G, Su L Z, Jia M and Chen W S. 2014. Fuzzy clustering with a modified MRF energy function for change detection in synthetic aperture radar images. IEEE Transactions on Fuzzy Systems, 22(1): 98–109
Gou S P, Zhuang X, Zhu H M and Yu T T. 2013. Parallel sparse spectral clustering for SAR image segmentation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(4): 1949–1963
Hichri H, Bazi Y, Alajlan N and Malek S. 2013. Interactive segmentation for change detection in multispectral remote-sensing images. IEEE Geoscience and Remote Sensing Letters, 10(2): 298–302
贾建华, 焦李成. 2010. 空间一致性约束谱聚类算法用于图像分割. 红外与毫米波学报, 29(1): 69–74
Jia J H and Jiao L C. 2010. Image segmentation by spectral clustering algorithm with spatial coherence constraints. Journal of Infrared and Millimeter Waves, 29(1): 69–74
Lang F K, Yang J, Li D R, Zhao L L and Shi L. 2014. Polarimetric SAR image segmentation using statistical region merging. IEEE Geoscience and Remote Sensing Letters, 11(2): 509–513
Lu D, Mausel P, Brondízio E and Moran E. 2004. Change detection techniques. International Journal of Remote Sensing, 25(12): 2365–2407
Nock R and Nielsen F. 2004. Statistical region merging. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(11): 1452–1458
Salmon J. 2010. On two parameters for denoising with non-local means. IEEE Signal Processing Letters, 17(3): 269–272
宋晓峰, 王爽, 刘芳. 2010. 基于区域MRF和贝叶斯置信传播的SAR图像分割. 电子学报, 38(12): 2810–2815
Song X F, Wang S and Liu F. 2010. SAR image segmentation using Markov random field based on regions and Bayes belief Propagation. Acta Electronica Sinica, 38(12): 2810–2815
Soni V, Bhandari A K, Kumar A and Singh G K. 2013. Improved sub-band adaptive thresholding function for denoising of satellite image based on evolutionary algorithms. IET Signal Processing, 7(8): 720–730
万红林, 焦李成, 辛芳芳. 2012. 基于交互式分割技术和决策级融合的SAR图像变化检测. 测绘学报, 41(1): 74–80
Wan H L, Jiao L C and Xin F F. 2012. Interactive segmentation technique and decision-level fusion based change detection for SAR image. Acta Geodaeticaet Cartographica Sinica, 41(1): 74–80
Wang B, Gao X B, Tao D C and Li X L. 2014. A nonlinear adaptive level set for image segmentation. IEEE Transactions on Cybernetics, 44(3): 418–428
Wang Y, Du L and Dai H. 2016. Unsupervised SAR image change detection based on SIFT keypoints and region information. IEEE Geoscience and Remote Sensing Letters, 13(7): 931–935
Xiong B L, Chen Q, Jiang Y M and Kuang G Y. 2012. A threshold selection method using two SAR change detection measures based on the Markov random field model. IEEE Geoscience and Remote Sensing Letters, 9(2): 287–291
相关作者
相关机构