收稿：2016-08-12，
录用：2017-9-7，
纸质出版：2018-01
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变化矢量分析CVA方法在中低分辨率遥感影像变化检测中已得到广泛应用，但由于高分辨率遥感影像存在不同地物尺度差异大、不同类别地物光谱相互重叠的问题，因此对于高分影像的变化检测具有局限性。为提高高分影像变化检测精度，提出了一种面向对象的多特征分级CVA变化检测方法，首先，利用基于区域邻接图的影像分割方法分别对两时相遥感影像进行多尺度分割，提取分割图斑的光谱、纹理和形状特征；然后，在各级尺度下，分别运用随机森林方法进行特征选择，计算CVA变化强度图；最后，根据信息熵对多级变化强度图进行自适应融合，利用Otsu阈值法检测变化区域，并与仅考虑光谱特征的分级CVA变化检测方法、像元级多特征CVA变化检测方法以及仅考虑光谱特征的像元级CVA变化检测方法进行比较分析。实验表明：与比较方法相比，本文方法的变化检测精度较高，误检率和漏检率较低。
With increasing image resolution
change detection for high-resolution images has become one of the most important aspects of remote sensing research. Change Vector Analysis (CVA) is an effective method that has been widely used in change detection for low- or moderate-resolution remote sensing images. However
processing high-resolution data involves limitations caused by spectral heterogeneity and objects with different scales. Thus
CVA must be combined with object-oriented methods. However
the performance of most object-oriented methods depends on the results of image segmentation
which are unstable due to the difficulty in determining the optimum scale. Taking advantage of the rich spatial information in high-resolution images is obviously important. Considering the aforementioned problems
this work proposes an object-oriented and multi-feature hierarchical change detection method based on CVA. Bi-temporal high-resolution remote sensing images are hierarchically segmented. Hierarchical image segmentation is realized with an image segmentation method based on a region adjacency graph. A logical OR is then applied to corresponding segmentation levels of the bi-temporal images. On the basis of spatial characteristics
spectral
texture
and shape features are extracted. A gray level co-occurrence matrix is used as a texture feature
and a geometric moment is used as a shape feature. Feature selection is realized with a random forest algorithm. Then
CVA is conducted to calculate the hierarchical magnitude images according to the optimal feature vectors. The final change magnitude image is obtained by fusing the hierarchical magnitude images using an adaptive fusion method. Finally
the Otsu algorithm is used to determine the change threshold values and thereby realize change detection. The change detection result of the object-oriented and multi-feature hierarchical CVA method is compared with those of the hierarchical CVA that only uses spectral features
the multi-feature pixel-based CVA
and the pixel-based CVA that only uses spectral features. Experiment outcomes show that the proposed method offers higher change detection accuracy and greater stability than the other three methods. It also achieves reduced false alarm rate and missing alarm rate in the change detection result. The proposed method can also reduce salt-and-pepper noise and produce entire changed objects. The unit for analysis in the proposed object-oriented and multi-feature hierarchical change detection based on CVA is an image object at different scales. The changes are detected at different hierarchy levels to adapt to the different characteristics of objects with different scales and to avoid the difficulty in determining the optimal segmentation scale. Thus
the impact of segmentation on change detection precision is minimized. In addition
multiple feature extraction and optimal feature selection ensure that the spectral and spatial information of high-resolution images is fully utilized while the characteristic redundancy is reduced. Therefore
the influences of complex spectral characters are avoided. The proposed method demonstrates high performance and high reliability.
Berberoğlu S, Akin A, Atkinson P M and Curran P J. 2010. Utilizing image texture to detect land-cover change in Mediterranean coastal wetlands. International Journal of Remote Sensing, 31(11): 2793–2815
Bovolo F. 2009. A multilevel parcel-based approach to change detection in very high resolution multitemporal images. IEEE Geoscience and Remote Sensing Letters, 6(1): 33–37
Bovolo F, Marchesi S and Bruzzone L. 2012. A framework for automatic and unsupervised detection of multiple changes in multitemporal images. IEEE Transactions on Geoscience and Remote Sensing, 50(6): 2196–2212
Bovolo F, Marin C and Bruzzone L. 2013. A hierarchical approach to change detection in very high resolution SAR images for surveillance applications. IEEE Transactions on Geoscience and Remote Sensing, 51(4): 2042–2054
Breiman L. 2001. Random forests. Machine Learning, 45(1): 5–32
Erener A and Düzgün H Ş. 2009. A methodology for land use change detection of high resolution pan images based on texture analysis. Italian Journal of Remote Sensing, 41(2): 47–59
冯文卿, 张永军. 2015. 利用多尺度融合进行面向对象的遥感影像变化检测. 测绘学报, 44(10): 1142–1151
Feng W Q and Zhang Y J. 2015. Object-oriented change detection for remote sensing images based on multi-scale fusion. Acta Geodaetica et Cartographica Sinica, 44(10): 1142–1151 (
Hussain M, Chen D M, Cheng A, Wei H and Stanley D. 2013. Change detection from remotely sensed images: from pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 80: 91–106
贾永红, 谢志伟, 张谦, 杨刚. 2015. 采用独立阈值的遥感影像变化检测方法. 西安交通大学学报, 49(12): 12–18
Jia Y H, Xie Z W, Zhang Q and Yang G. 2015. A change detection method of remote sensing images with independent threshold. Journal of Xi'an Jiaotong University, 49(12): 12–18 (
Li L, Ying G W, Wen X H and Zhang Y. 2015. Object-oriented change detection based on spatiotemporal relationship in multitemporal remote-sensing images//ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Berlin, Germany: ISPRS: 1241–1248 [DOI: 10.5194/isprsarchives-XL-7-W3-1241-2015]
Mateos C J B, Ruiz C P, Crespo R G and Sanz A G C. 2010. Relative radiometric normalization of multitemporal images. International Journal of Artificial Intelligence and Interactive Multimedia, 1(3): 54–59
Newsam S D and Kamath C. 2005. Comparing shape and texture features for pattern recognition in simulation data.//Proceedings Volume 5672, Image Processing: Algorithms and Systems IV. San Jose, California, United States: SPIE: 106–117 [DOI: 10.1117/12.587057]
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Louppe G, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M and Duchesnay É. 2011. Scikit-learn: machine learning in Python. Journal of Machine Learning Research, 12: 2825–2830
Qi Z X, Yeh A G O, Li X and Zhang X H. 2015. A three-component method for timely detection of land cover changes using polarimetric SAR images. ISPRS Journal of Photogrammetry and Remote Sensing, 107: 3–21
全卫澎, 李卫华, 李小春. 2015. 多特征自适应融合的高分辨率遥感影像变化检测. 电光与控制, 22(3): 45–49
Quan W P, Li W H and Li X C. 2015. Detecting the change of high-resolution remote sensing images by adaptive multi-feature fusion. Electronics Optics and Control, 22(3): 45–49 (
Tremeau A and Colantoni P. 2000. Regions adjacency graph applied to color image segmentation. IEEE Transactions on Image Processing, 9(4): 735–744
Van Der Walt S, Schönberger J L, Nunez-Iglesias J, Boulogne F, Warner J D, Yager N, Gouillart E and Yu T. 2014. Scikit-image: image processing in Python. PeerJ, 2(2): e453
王东广, 肖鹏峰, 宋晓群, 王铁成, 陈刚. 2012. 结合纹理信息的高分辨率遥感图像变化检测方法. 国土资源遥感, 24(4): 76–81
Wang D G, Xiao P F, Song X Q, Wang T C and Chen G. 2012. Change detection method for high resolution remote sensing image in association with textural and spectral information. Remote Sensing for Land and Resources, 24(4): 76–81 (
王丽云, 李艳, 汪禹芹. 2014. 基于对象变化矢量分析的土地利用变化检测方法研究. 地球信息科学学报, 16(2): 307–313
Wang L Y, Li Y and Wang Y Q. 2014. Research on land use change detection based on an object-oriented change vector analysis method. Journal of Geo-Information Science, 16(2): 307–313 (
闫利, 巩翼龙, 张毅, 段伟. 2014. 光流动态纹理在土地利用/覆盖变化检测研究中的应用. 光谱学与光谱分析, 34(11): 3056–3061
Yan L, Gong Y L, Zhang Y and Duan W. 2014. Application of optical flow dynamic texture in land use/cover change detection. Spectroscopy and Spectral Analysis, 34(11): 3056–3061 (
Zhang P L, Lv Z Y and Shi W Z. 2014. Local spectrum-trend similarity approach for detecting land-cover change by using SPOT-5 satellite images. IEEE Geoscience and Remote Sensing Letters, 11(4): 738–742
Zhao Y D and Du P J. 2009. Incorporating spectral, texture, and shape information for high spatial resolution satellite imagery classification//Proceedings Volume 7147, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images. Guangzhou, China: SPIE: 1–8 [DOI: 10.1117/12.813246]
赵忠明, 孟瑜, 岳安志, 黄青青, 孔赟珑, 袁媛, 刘晓奕, 林蕾, 张蒙蒙. 2016. 遥感时间序列影像变化检测研究进展. 遥感学报, 20(5): 1110–1125
Zhao Z M, Meng Y, Yue A Z, Huang Q Q, Kong Y L, Yuan Y, Liu X Y, Lin L and Zhang M M. 2016. Review of remotely sensed time series data for change detection. Journal of Remote Sensing, 20(5): 1110–1125 (
Zhuang H F, Deng K Z, Fan H D and Yu M. 2016. Strategies combining spectral angle mapper and change vector analysis to unsupervised change detection in multispectral images. IEEE Geoscience and Remote Sensing Letters, 13(5): 681–685
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