基于水平集演化和支持向量机分类的高分辨率遥感图像自动变化检测
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摘要
提出了基于水平集演化和支持向量机(SVM)分类的高分辨率遥感图像变化检测方法,该方法将像素级的和对象级的变化检测方法相结合,运用了像素特征和对象特征以提高变化类和非变化类的准确率。在像素级上,变化检测问题转化为水平集演化的图像分割问题。在对象级上,本文可以从分割结果中为SVM分类器自动地选择潜在的训练样本。最终将基于像素级的变化和基于对象级的变化相结合得到最终的变化结果。所提出的方法的主要优势在于可以自动选择合适的样本进行SVM分类器训练。此外,提出的方法可以有效的提高精确度和自动化水平。通过SPOT5图像和航空图像进行实验,结果表明该方法是有效的。
We propose a method for change detection in high-resolution remote sensing images by means of level set evolution and Support Vector Machine(SVM) classification, which combined both pixel-level method and object-level method. Both pixel-based change features and object-based ones are extracted to improve the discriminability between the changed class and the unchanged class.At the pixel-level, the change detection problem is formulated as a segmentation issue using level set evolution in the difference image. At the object-level, potential training samples are selectedfrom the segmentation results without manual intervention into SVM classifier. Thereafter, the final changes are obtained by combining the pixel-based changes and the object-based changes. A chief advantage of our approach is being able to select appropriate samples for SVM classifier training. Furthermore, our proposed method helps improving the accuracy and the degree of automation. We systematically evaluated it with a variety of SPOT5 images and aerial images. Experimental results demonstrated the accuracy of our proposed method.
We propose a method for change detection in high-resolution remote sensing images by means of level set evolution and Support Vector Machine(SVM) classification, which combined both pixel-level method and object-level method. Both pixel-based change features and object-based ones are extracted to improve the discriminability between the changed class and the unchanged class.At the pixel-level, the change detection problem is formulated as a segmentation issue using level set evolution in the difference image. At the object-level, potential training samples are selectedfrom the segmentation results without manual intervention into SVM classifier. Thereafter, the final changes are obtained by combining the pixel-based changes and the object-based changes. A chief advantage of our approach is being able to select appropriate samples for SVM classifier training. Furthermore, our proposed method helps improving the accuracy and the degree of automation. We systematically evaluated it with a variety of SPOT5 images and aerial images. Experimental results demonstrated the accuracy of our proposed method.
引文
[1] CHANG Ni-Bin, HAN Min. Change Detection of Land Use and Land Cover in an Urban Region with SPOT-5 Images and Partial Lanczos Extreme Learning Machine[J]. Journal of Applied Remote Sensing, 4, 043551, 2010(4):1-15.
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[6] ROUHOLLAH Dianat, SHOHREH Kasaei. Change Detection in Remote Sensing Images Using Modified Polynomial Regression and Spatial Multivariate Alteration Detection[J]. Journal of Applied Remote Sensing, 2009(3): 033561.
[7] BAO Nisha, LECHNER Alex M. Comparison of Relative Radiometric Normalization Methods Using Pseudo-invariant Features for Change Detection Studies in Rural and Urban Landscapes[J]. Journal of Applied Remote Sensing, 2012(6): 063578.
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[9] CELIK Turgay, MA Kai-Kuang. Multitemporal Image Change Detection Using Undecimated Discrete Wavelet Transform and Active Contours[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(2): 706.
[10] PIJANOWSKI B C, PITHADIA S, SHELLITO B A, et al. Calibrating a Neural Network-based Urban Change Model for Two Metropolitan Areas of the Upper Midwest of the United States[J]. International Journal of Geographical Information Science, 2005(19):197.
[11] HUO Chunlei, ZHOU Zhixin, LU Hanqing, et al. Fast Object-level Change Detection for VHR Images[J]. IEEE GeoScience and Remote Sensing Letters, 2010, 7(1):118.
[12] VOLPI Michele, TUIA Devis, BOVOLO Francesca, et al. Supervised Change Detection in VHR Images Using Contextual Information and Support Vector Machines[J]. International Journal of Applied Earth Observation and Geoinformation, 2011(20):77.
[13] IM J, JENSEN J R, TULLIS J A. Object-based Change Detection Using Correlation Image Analysis and Image Segmentation[J]. International Journal of Remote Sensing, 2008, 29(2):399.
[14] SU W, LI J, CHEN Y, et al. Textural and Local Spatial Statistics for the Object-oriented Classification of Urban Areas Using High Resolution Imagery[J]. International Journal of Remote Sensing, 2008(29): 3105.
[15] HUSSAIN Masroor, CHEN Dongmei, CHENG Angela, et al. Change Detection from Remotely Sensed Images:From Pixel-based to Object-based Approaches[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013(80): 91.
[16] CHEN Gang, GEOFFREY J Hay, LUIS M T, et al. Object-based Change Detection[J]. International Journal of Remote Sensing, 2012,33(14):4434.
[17] BAZI Y, BRUZZONE L, MELGANI F. Unsupervised Change Detection in Multispectral Using Level Set Methods[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010,48(8): 3178.
[18] BOVOLO F, BRUZZONE L, MARCHESI Silvia. Analysis and Adaptive Estimation of the Registration Noise Distribution in Multitemporal VHR Images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009,47(8): 2658.
[19] ZHANG Jieyu, QIANG Chen. A Highly Repeatable Feature Detector: Improved Harris-Laplace[J]. Multimedia Tools and Applications, 2011(52):175.
[20] FELZENSZWALB P F, HUTTENLOCHER D. Efficient Graph-based Image Segmentation[J]. International Journal of Computer Vision, 2010, 59(2):167.
[2] GHOSH A, MISHRA N S, GHOSH S. Fuzzy Clustering Algorithms for Unsupervised Change Detection in Remote Sensing Images[J]. Information Sciences, 2011(181):699.
[3] JI W, MA J, TWIBELL R W, et al. Characterizing Ubran Sprawl Using Multi-stage Remote Sensing Images and Landscape Metrics[J]. Computers, Environment and Ubran Systems, 2006(30): 861.
[4] BRUZZONE L,PRIETO D F. Automatic Analysis of the Difference Image for Unsupervised Change Detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000(38):1171.
[5] IM J, JENSEN J R, MICHAEL E. Optimizing the Binary Discriminant Function in Change Detection Applications[J]. Remote Sensing of Environment, 2008(112):2761.
[6] ROUHOLLAH Dianat, SHOHREH Kasaei. Change Detection in Remote Sensing Images Using Modified Polynomial Regression and Spatial Multivariate Alteration Detection[J]. Journal of Applied Remote Sensing, 2009(3): 033561.
[7] BAO Nisha, LECHNER Alex M. Comparison of Relative Radiometric Normalization Methods Using Pseudo-invariant Features for Change Detection Studies in Rural and Urban Landscapes[J]. Journal of Applied Remote Sensing, 2012(6): 063578.
[8] BAZI Y, MELGANI F. A Variational Level-set Method for Unsupervised Change Detection in Remote Sensing Images[J]. In Proc. IEEE Int. Conf. Geosci. Remote Sens. Symp., 2009(2): 984.
[9] CELIK Turgay, MA Kai-Kuang. Multitemporal Image Change Detection Using Undecimated Discrete Wavelet Transform and Active Contours[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(2): 706.
[10] PIJANOWSKI B C, PITHADIA S, SHELLITO B A, et al. Calibrating a Neural Network-based Urban Change Model for Two Metropolitan Areas of the Upper Midwest of the United States[J]. International Journal of Geographical Information Science, 2005(19):197.
[11] HUO Chunlei, ZHOU Zhixin, LU Hanqing, et al. Fast Object-level Change Detection for VHR Images[J]. IEEE GeoScience and Remote Sensing Letters, 2010, 7(1):118.
[12] VOLPI Michele, TUIA Devis, BOVOLO Francesca, et al. Supervised Change Detection in VHR Images Using Contextual Information and Support Vector Machines[J]. International Journal of Applied Earth Observation and Geoinformation, 2011(20):77.
[13] IM J, JENSEN J R, TULLIS J A. Object-based Change Detection Using Correlation Image Analysis and Image Segmentation[J]. International Journal of Remote Sensing, 2008, 29(2):399.
[14] SU W, LI J, CHEN Y, et al. Textural and Local Spatial Statistics for the Object-oriented Classification of Urban Areas Using High Resolution Imagery[J]. International Journal of Remote Sensing, 2008(29): 3105.
[15] HUSSAIN Masroor, CHEN Dongmei, CHENG Angela, et al. Change Detection from Remotely Sensed Images:From Pixel-based to Object-based Approaches[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013(80): 91.
[16] CHEN Gang, GEOFFREY J Hay, LUIS M T, et al. Object-based Change Detection[J]. International Journal of Remote Sensing, 2012,33(14):4434.
[17] BAZI Y, BRUZZONE L, MELGANI F. Unsupervised Change Detection in Multispectral Using Level Set Methods[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010,48(8): 3178.
[18] BOVOLO F, BRUZZONE L, MARCHESI Silvia. Analysis and Adaptive Estimation of the Registration Noise Distribution in Multitemporal VHR Images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009,47(8): 2658.
[19] ZHANG Jieyu, QIANG Chen. A Highly Repeatable Feature Detector: Improved Harris-Laplace[J]. Multimedia Tools and Applications, 2011(52):175.
[20] FELZENSZWALB P F, HUTTENLOCHER D. Efficient Graph-based Image Segmentation[J]. International Journal of Computer Vision, 2010, 59(2):167.