基于Relief-PCA特征选择的遥感图像变化检测
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摘要
面向对象的变化检测技术在高分辨率遥感图像领域已经得到广泛地应用。由于遥感图像受光照、大气环境等成像条件的影响,图像特征的质量也参差不齐,筛选出高质量的特征成为对象级遥感图像变化检测的关键。针对此问题,提出了一种基于Relief-PCA特征选择的对象级遥感图像变化检测方法。首先,对原始图像进行多尺度分割获得目标对象,并提取对象的光谱特征与纹理特征;然后,利用对数比值法获得变化矢量,再使用Relief-PCA特征选择的方法对图像的对象特征进行筛选与降维;最后,计算并生成CVA变化强度图,利用Otsu方法对变化强度图进行阈值分割得到最终的变化检测结果。实验表明:与已有方法相比,该方法的变化检测精度更高,误检率和漏检率更低。
Object-oriented change detection technology has been widely used in the field of high-resolution remote sensing images. As the remote sensing images are affected by imaging conditions such as illumination, atmospheric environment and other factors, the quality of image features also varies. Selecting high-quality features becomes the key of the change detection of remote sensing image at the object level. For the above problems, a change detection method of object-level remote sensing images based on Relief-PCA feature selection has been proposed. In the proposed method, first of all, the original image is multi-scaled to obtain the target object. Afterwards,the spectral features and texture features of the object are extracted. Then a logarithmic ratio method is used to obtain the change vector, and the object features of the original image are filtered and dimensioned through the Relief-PCA feature selection method. Finally, the change vector analysis(CVA) variation intensity map is calculated and generated. The Otsu method is used to conduct the threshold segmentation of the variation intensity map to obtain the final change detection result.Experimental results show that compared with other state-of-the-art methods, the proposed method has higher detection accuracy, lower misdetection rate and lower missed detection rate.
Object-oriented change detection technology has been widely used in the field of high-resolution remote sensing images. As the remote sensing images are affected by imaging conditions such as illumination, atmospheric environment and other factors, the quality of image features also varies. Selecting high-quality features becomes the key of the change detection of remote sensing image at the object level. For the above problems, a change detection method of object-level remote sensing images based on Relief-PCA feature selection has been proposed. In the proposed method, first of all, the original image is multi-scaled to obtain the target object. Afterwards,the spectral features and texture features of the object are extracted. Then a logarithmic ratio method is used to obtain the change vector, and the object features of the original image are filtered and dimensioned through the Relief-PCA feature selection method. Finally, the change vector analysis(CVA) variation intensity map is calculated and generated. The Otsu method is used to conduct the threshold segmentation of the variation intensity map to obtain the final change detection result.Experimental results show that compared with other state-of-the-art methods, the proposed method has higher detection accuracy, lower misdetection rate and lower missed detection rate.
引文
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[23]WALTER V.Object-based classification of remote sensing data for change detection[J].ISPRS Journal of Photogrammetry and Remote Sensing,2004,58(3-4):225-238.
[2]王文杰,赵忠明,朱海青.面向对象特征融合的高分辨率遥感图像变化检测方法[J].计算机应用研究,2009,26(8):3149-3151.
[3]BOVOLO F,BRUZZONE L.A theoretical framework for unsupervised change detection based on change vector analysis in the polar domain[J].IEEETransactions on Geoscience and Remote Sensing,2007,45(1):218-236.
[4]JOHNSON R D,KASISCHKE E S.Change vector analysis:A technique for the multispectral monitoring of land cover and condition[J].International Journal of Remote Sensing,1998,19(3):411-426.
[5]CARVALHO JúNIOR O A,GUIMARAES R F,Gillespie A R,et al.A new approach to change vector analysis using distance and similarity measures[J].Remote Sensing,2011,3(11):2473-2493.
[6]RADKE R J,ANDRA S,AL-KOFAHI O,et al.Image change detection algorithms:A systematic survey[J].IEEE Transactions on Image Processing,2005,14(3):294-307.
[7]佃袁勇,方圣辉,姚崇怀.多尺度分割的高分辨率遥感影像变化检测[J].遥感学报,2016,20(1):129-137.
[8]MOHANAIAH P,SATHYANARAYANA P,Gurukumar L.Image texture feature extraction using GLCMapproach[J].International Journal of Scientific and Research Publications,2013,3(5):1-5.
[9]HUSSAIN M,CHEN D,CHENG A,et al.Change detection from remotely sensed images:From pixel-based to object-based approaches[J].ISPRSJournal of Photogrammetry and Remote Sensing,2013,80:91-106.
[10]王丽云,李艳,汪禹芹.基于对象变化矢量分析的土地利用变化检测方法研究[J].地球信息科学学报,2014,16(2):307-313.
[11]石善球.一种面向对象的CVA变化检测方法[J].测绘与空间地理信息,2017,40(11):80-82.
[12]MATEOS C J B,RUIZ C P,CRESPO R G,et al.Relative radiometric normalization of multitemporal images[J].International Journal of Interactive Multimedia and Artificial Intelligence,2010,1(3):53-58.
[13]LIANG L,YING G,WEN X,et al.Object-oriented change detection based on spatiotemporal relationship in multitemporal remote-sensing images[J].International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sclences,2015,XL-7/W3(7):1241-1248.
[14]SWINIARSKI R W,SKOWRON A.Rough set methods in feature selection and recognition[J].Pattern Recognition Letters,2003,24(6):833-849.
[15]KIRA K,RENDELL L A.A practical approach to feature selection[C]//Proceedings of the 9th International Workshop on Machine Learning.San Francisco:Morgan Kaufmann Publishers Inc,1992:249-256.
[16]ZHANG J,LIN H,ZHAO M.A fast algorithm for hand gesture recognition using relief[C]//2009 6th International Conference on Fuzzy Systems and Knowledge Discovery.New York:IEEE Press,2009,1:8-12.
[17]ROBNIK-SIKONJA M,KONONENKO I.Theoretical and empirical analysis of ReliefF and RReliefF[J].Machine Learning,2003,53(1-2):23-69.
[18]SUN Y.Iterative RELIEF for feature weighting:Algorithms,theories,and applications[J].IEEETransactions on Pattern Analysis and Machine Intelligence,2007,29(6):1035-1051.
[19]FARRELL M D,MERSEREAU R M.On the impact of PCA dimension reduction for hyperspectral detection of difficult targets[J].IEEE Geoscience and Remote Sensing Letters,2005,2(2):192-195.
[20]STEFANO L D,MATTOCCIA S,Mola M.Achange-detection algorithm based on structure and colour[C]//IEEE Conference on Advanced Video and Signal Based Surveillance.Washington,DC:IEEEComputer Society Press,2003:252.
[21]QIU B,PRINET V,PERRIER E,et al.Multi-block PCAmethod for image change detection[C]//International Conference on Image Analysis and Processing,2003.New York:IEEE Press,2003:385.
[22]HALL O,HAY G J.A multiscale object-specific approach to digital change detection[J].International Journal of Applied Earth Observation and Geoinformation,2003,4(4):311-327.
[23]WALTER V.Object-based classification of remote sensing data for change detection[J].ISPRS Journal of Photogrammetry and Remote Sensing,2004,58(3-4):225-238.