基于对象BOW特征的高分辨率遥感影像变化检测方法
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摘要
针对传统基于像素的变化检测方法的缺点,以及底层特征表现能力不足等问题,提出一种基于对象BOW特征的变化检测方法。首先,将经过预处理操作的两期影像进行波段组合得到组合后影像,再考虑地物光谱特征和几何空间信息对组合后影像进行多尺度分割,获得相对应的对象基元;同时,分别提取两幅影像的底层特征(包括影像各波段的均值和方差以及灰度图像的6种纹理特征)。其次,将对象视作文档,像素的特征向量视作单词,利用BOW模型构建影像对象的中层表达,即对象的BOW特征。最后,通过相似性度量算法比较相应对象的BOW特征,从而识别出影像上的变化区域。本文利用2组WorldView-2影像进行了检验,结果表明本文方法的变化检测结果较为完整,精度优于对比方法。本文方法基本能够满足变化检测的需求,为高分辨率遥感影像上的数据挖掘分析提供了有效的手段。
A novel change detection method is proposed based on BOW(Bag of Words) features of the image objects, aiming at the drawbacks of traditional change detection methods and the poor interpretation capacity of basic features. First of all, a simultaneous segmentation method which took both spectral features and geometric information into consideration was applied to two images that were previously preprocessed and layer-stacked to acquire corresponding image objects; then mathematical mean and variance of every image band, and six textures from its panchromatic image were extracted as basic features. Moreover, the BOW representation of each image object was constructed by regarding objects as documents and basic features of pixels as words.Finally, similarity measurement was used to compare BOW features to define changed and no-changed areas. In this paper, experimental results on two sets of World View-2 images showed that the proposed method demonstrated superiority in completeness of detection result and outperformed the methods for comparison in accuracy assessment. In all, the proposed method can basically meet the needs of change detection work and provide an effective way for data mining and analysis of high resolution remote sensing images.
A novel change detection method is proposed based on BOW(Bag of Words) features of the image objects, aiming at the drawbacks of traditional change detection methods and the poor interpretation capacity of basic features. First of all, a simultaneous segmentation method which took both spectral features and geometric information into consideration was applied to two images that were previously preprocessed and layer-stacked to acquire corresponding image objects; then mathematical mean and variance of every image band, and six textures from its panchromatic image were extracted as basic features. Moreover, the BOW representation of each image object was constructed by regarding objects as documents and basic features of pixels as words.Finally, similarity measurement was used to compare BOW features to define changed and no-changed areas. In this paper, experimental results on two sets of World View-2 images showed that the proposed method demonstrated superiority in completeness of detection result and outperformed the methods for comparison in accuracy assessment. In all, the proposed method can basically meet the needs of change detection work and provide an effective way for data mining and analysis of high resolution remote sensing images.
引文
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[24]孙开敏,陈艳.对象级变化检测中的变化向量分析法[C].The International Conference on Computational Intelligence and Industrial Application,2010.[Sun K M,Chen Y.The application of objects change vector analysis in object-level change detection[C].The International Conference on Computational Intelligence and Industrial Application,2010.]
[2]林祥国,张继贤.面向对象的形态学建筑物指数及其高分辨率遥感影像建筑物提取应用[J].测绘学报,2017,46(6):724-733.[Lin X G,Zhang J X.Object-based morphological building index for building extraction from high resolution remote sensing imagery[J].Acta Geodaetica et Cartographica Sinica,2017,46(6):724-733.]
[3]朱长青,王耀革,马秋禾,等.基于形态分割的高分辨率遥感影像道路提取[J].测绘学报,2004,33(4):347-351.[Zhu CQ,Wang Y G,Ma Q H,et al.Road extraction from highresolution remotely sensed image based on morphological segmentation[J].Acta Geodaetica et Cartographica Sinica,2004,33(4):347-351.]
[4]Hussain M.,Chen D,Cheng A,et al.Change detection from remotely sensed images:From pixel-based to objectbased approaches[J].ISPRS Journal of Photogrammetry&Remote Sensing,2013,80(2):91-106.
[5]Huang X,Zhang L P,Li P.Classification and extraction of spatial features in urban areas using high-resolution multispectral imagery[J].IEEE Geoscience&Remote Sensing Letters,2007,4(2):260-264.
[7]Blaschke T.Object based image analysis for remote sensing[J].ISPRS Journal of Photogrammetry&Remote Sensing,2010,65(1):2-16.
[8]刘纯,洪亮,陈杰,等.融合像素-多尺度区域特征的高分辨率遥感影像分类算法[J].遥感学报,2015,19(2):228-239.[Liu C,Hong L,Chen J,et al.Fusion of pixel-based and multi-scale region-based features for the classification of high-resolution remote sensing image[J].Journal of Remote Sensing,2015,19(2):228-239.]
[9]崔林丽,唐娉,赵忠明.一种基于对象和多种特征整合的分类识别方法研究[J].遥感学报,2006,10(1):104-110.[Cui L L,Tang P,Zhao Z M.Study on object-oriented classification method by integrating various features[J].Journal of Remote Sensing,2006,10(1):104-110.]
[10]Hazel G G.Object-level change detection in spectral imagery[J].IEEE Transactions on Geoscience&Remote Sensing,2001,39(3):553-561.
[11]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 Y Y,Fang S H,Yao C H.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.]
[12]王琰,舒宁,龚龑.高分辨率遥感影像土地利用变化检测方法研究[J].国土资源遥感,2012,24(1):43-47.[Wang Y,Shu N,Gong Y.A study of land use change detection based on high resolution remote sensing images[J].Remote Sensing of Land and Resources,2012,24(1):43-47.]
[13]冯文卿,张永军.利用多尺度融合进行面向对象的遥感影像变化检测[J].测绘学报,2015,44(10):1142-1151.[Feng W Q,Zhang Y J.Object-oriented change detection for remote sensing images based on multi-scale fusion[J].Acta Geodaetica et Cartographica Sinica,2015,44(10):1142-1151.]
[14]方涛,霍宏,马贺平.高分辨率遥感影像智能解译[M].北京:科学出版社.2016.[Fang T,Huo H,Ma H P.Intelligent interpretation of high resolution remote sensing images[M].Beijing:Science Press,2016.]
[15]闫利,朱睿希,刘异,等.顾及遥感影像场景类别信息的视觉单词优化分类[J].遥感学报,2017,21(2):280-290.[Yan L,Zhu R X,Liu Y,et al.Scene classification of remote sensing images by optimizing visual vocabulary concerning scene label information[J].Remote Sensing of Land and Resources,2017,21(2):280-290.]
[16]赵理君,唐娉,霍连志,等.图像场景分类中视觉词包模型方法综述[J].中国图象图形学报,2014,19(3):333-342.[Zhao L J,Tang P,Huo L Z,et al.Review of the bag-ofvisual-words models in image scene classification[J].Journal of Image and Graphics,2014,19(3):333-342.]
[17]Leung T,Malik J.Representing and recognizing the visual appearance of materials using three-dimensional textons[J].International Journal of Computer Vision,2001,43(1):29-44.
[18]Huang X,Liu H,Zhang L.Spatiotemporal detection and analysis of urban villages in Mega City Regions of China using high-resolution remotely sensed imagery[J].IEEETransactions on Geoscience&Remote Sensing,2015,53(7):3639-3657.
[19]Wen D,Huang X,Zhang L,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.
[20]Wu C,Zhang L F,Zhang L P.A scene change detection framework for multi-temporal very high resolution remote sensing images[J].Signal processing,2016,124:184-197.
[21]Shao W,Yang W,Xia G S,et al.A hierarchical scheme of multiple feature fusion for high-resolution satellite scene categorization[C].Computer Vision Systems,Germany:Springer Berlin Heidelberg,2013:324-333.
[22]Haralick R M.Texture features for image classification[J].IEEE Transactions on Systems Man and Cybernetics,1973,3(6):610-621.
[23]徐盛.基于主题模型的高空间分辨率遥感影像分类研究[D].上海:上海交通大学,2012.[Xu S.Topic model on image classification and their applications in high spatial resolution remote sensing image[D].Shanghai:Shanghai Jiao Tong University,2012.]
[24]孙开敏,陈艳.对象级变化检测中的变化向量分析法[C].The International Conference on Computational Intelligence and Industrial Application,2010.[Sun K M,Chen Y.The application of objects change vector analysis in object-level change detection[C].The International Conference on Computational Intelligence and Industrial Application,2010.]