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山东大学学报(工学版) ›› 2018, Vol. 48 ›› Issue (3): 1-9.doi: 10.6040/j.issn.1672-3961.0.2017.406

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基于正反向异质性的遥感图像变化检测

李士进,王声特,黄乐平   

  1. 河海大学计算机与信息学院, 江苏 南京 210098
  • 收稿日期:2017-05-09 出版日期:2018-06-20 发布日期:2017-05-09
  • 作者简介:李士进(1973— ),男,江苏泰州人,教授,博士,主要研究方向为模式识别与计算机视觉. E-mail:lishijin@hhu.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(61170200);江苏省重点研发计划(社会发展)资助项目(BE2015707)

Change detection with remote sensing images based on forward-backward heterogenicity

LI Shijin, WANG Shengte, HUANG Leping   

  1. College of Computer and Information, Hohai University, Nanjing 210098, Jiangsu, China
  • Received:2017-05-09 Online:2018-06-20 Published:2017-05-09

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摘要： 为提高水体周边环境的变化检测结果的精度,提出一种改进的变化检测方法。在光谱与纹理特征结合的基础上融合指数特征构建混合特征空间,采用超像素生成算法(simple linear iterative cluster, SLIC)处理叠加影像获取地物对象,并综合地物对象的正反向异质信息构建地物对象的正反向异质性;使用最大数学期望算法与贝叶斯最小错误率理论获取两时相的变化信息,排除植被伪变化信息,形成相对准确和鲁棒的检测结果。试验结果表明:该方法能够有效区分水体周边环境中感兴趣的地物变化信息与不感兴趣的干扰信息、“伪变化信息”等,虚检率和漏检率较低,且正确率较高为96%以上,能够智能发现湖库水域周边“非正常”土地利用变化。

关键词: 水体, 变化检测, 正反向异质性, 超像素生成算法, 混合特征空间

Abstract: In order to improve the change detection accuracy of water surrounding environment, an improved change detection method was proposed. This method was based on the combination of spectral and textural features, and fused the index feature to construct a hybrid feature space. The simple linear iterative cluster(SLIC)algorithm was used to obtain ground objects by processing the superimposed images. Meanwhile, the proposed method synthesized various forward-backward heterogeneity information to construct the forward-backward heterogeneity of ground objects. The EM algorithm and the minimum error Bayes decision theory were used to obtain the change information of the images on two phases. By eliminating the pseudo change information of vegetation, the relative robust and exact detection results could be achieved. Experimental results showed that the proposed method could effectively distinguish the useful change information from uninterested disturbance information and pseudo change information, and had low false detection ratio and low missing detection ratio. The accuracy of the proposed method could reach more than 96%. Moreover, this method could intelligently recognize the abnormal land-use changes around lakes and reservoirs.

Key words: water body, simple linear iterative cluster, hybrid feature space, forward-backward heterogenicity, change detection

中图分类号: 

  • TP753
[1] WEN Dawei, HUANG Xin, ZHANG Liangpei, et al. A novel automatic change detection method for urban high-resolution remotely sensed imagery based on multiindex scene representation[J]. IEEE Transactions on Geoscience & Remote Sensing, 2015, 54(1):609-625.
[2] 佟国峰,李勇,丁伟利,等.遥感影像变化检测算法综述[J].中国图象图形学报,2015,20(12):1561-1571. TONG Guofeng, LI Yong, DING Weili, et al. Review of remote sensing image change detection[J]. Journal of Image & Graphics, 2015, 20(12):1561-1571.
[3] 辛芳芳,焦李成,王桂婷,等.基于小波域Fisher分类器的SAR图像变化检测[J].红外与毫米波学报,2011,30(2):173-178. XIN Fangfang, JIAO Licheng, WANG Guiting, et al. Change detection of SAR images based on wavelet domain Fisher classifier[J]. Journal of Infrared & Millimeter Waves, 2011, 30(2):173-178.
[4] KOMEIL R, ANUAR A, KARIM S, et al. A new approach for surface water change detection: Integration of pixel level image fusion and image classification techniques[J]. International Journal of Applied Earth Observation & Geoinformation, 2015, 34:226-234.
[5] 张永梅,季艳,马礼,等.遥感图像建筑物识别及变化检测方法[J].电子学报,2014,42(4):653-657. ZHANG Yongmei, JI Yan, MA Li, et al. A recniction and change detection method for building in remote sensing images[J]. Acta Eletronica Sinica, 2014, 42(4):653-657.
[6] CARLO M, FRANCESCA B, LORENZO B. Building change detection in multi-temporal very high resolution SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(5):2664-2682.
[7] 佃袁勇,方圣辉,姚崇怀.一种面向地理对象的遥感影像变化检测方法[J].武汉大学学报:信息科学版,2014,39(8):906-912. DIAN Yuanyong, FANG Shenghui, YAO Chonghuai. The geographic object-based method for change detection with remote sensing imagery[J]. Geomatics and Information Science of Wuhan University, 2014, 39(8):906-912.
[8] 陆苗,梅洋,赵勇,等.利用多尺度几何特征向量的变化检测方法[J].武汉大学学报:信息科学版,2015,40(5):623-627. LU Miao, MEI Yang, ZHAO Yong, et al. Change detection based on multi-scale geometric feature vector[J]. Geomatics and Information Science of Wuhan University, 2015, 40(5):623-627.
[9] FENG Wengqing, SUI Haigang, TU Jihui. Object-oriented change detection for remote sensing images based on multi-scale fusion[J]. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, XLI-B7(10):483-491.
[10] RADHAKRISHNA A, APPU S, AURELIEN L, et al. SLIC superpixels compared to state-of-the-art superpixel methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11):2274-2282.
[11] 李亮,舒宁,王凯,等.融合多特征的遥感影像变化检测方法[J].测绘学报,2014,43(9):945-953. LI Liang, SHU Ning, WANG Kai, et al. Change detection method for remote sensing images based on multi-features fusion[J]. Acta Geodaetica Et Cartographica Sinica, 2014, 43(9):945-953.
[12] HU Zhongwen, ZOU Qiu, LI Qingquan. Watershed superpixel[C] // IEEE International Conference on Image Processing(ICIP). New York, USA:IEEE Press, 2015:349-353.
[13] ZDENEK K, JIRI M, KRYSTIAN M. Tracking-learning-detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7):1409-1422.
[14] ZDENEK K, KRYSTIAN M, JIRI M. Forward-backward error: automatic detection of tracking failures[C] //2010 International Conference on Pattern Recognition(ICPR). New York, USA:IEEE Press, 2010: 2756-2759.
[15] OJALA T, PIETIKAINEN M, MAENPAA T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2000, 24(7):404-420.
[16] MCFEETERS S K. The use of normalized difference water index(NDWI)in the delineation of open water features[J]. International Journal of Remote Sensing, 1996, 17(7):1425-1432.
[17] HUETE A R. A soil-adjusted vegetation index(SAVI)[J]. Remote Sensing of Environment, 1988, 25(3):295-309.
[18] 贾永红,谢志伟,吕臻,等.一种新的遥感影像变化检测方法[J].武汉大学学报:信息科学版,2016,41(8):1001-1006. JIA Yonghong, XIE Zhiwei, LYU Zhen, et al. A new change detection method of remote sensing image[J]. Geomatics and Information Science of Wuhan University, 2016, 41(8):1001-1006.
[19] 李亮,龚龑,李雪,等.像斑直方图相似性测度的高分辨率遥感影像变化检测[J].遥感学报,2014,18(1):139-153. LI Liang, GONG Yan, LI Xue, et al. Change detection based on similarity measurement of object histogram using high-resolution remote sensing imagery[J]. Journal of Remote Sensing, 2014, 18(1):139-153.
[20] MELNYKOV V, IGOR M. Initializing the EM algorithm in Gaussian mixture models with an unknown number of components[J]. Computational Statistics & Data Analysis, 2012, 56(6):1381-1395.
[21] 黄春波,佃袁勇,周志翔,等.基于时间序列统计特性的森林变化监测[J].遥感学报,2015,19(4):657-668. HUANG Chunbo, DIAN Yuanyong, ZHOU Zhixiang, et al. Forest change detection based on time series images with statistical properties[J]. Joural of Remote Sensing, 2015, 19(4):657-668.
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