Abstract
Change detection is a fundamental task in the interpretation and understanding of remote sensing images. The aim is to partition the difference images acquired from multitemporal satellite images into changed and unchanged regions. Level set method is a promising way for remote sensing images change detection among the existed methods. Unfortunately, re-initialization, a necessary step in classical level set methods is known a complex and time-consuming process, which may limits their practical application in remote sensing images change detection. In this paper, we present an unsupervised change detection approach for remote sensing image based on an improved region-based active contour model without re-initialization. In order to eliminate the process for re-initialization and reduce the numerical errors caused by re-initialization, we describe an improving level set method for remote sensing images change detection. The proposed method introduced a distance regularization term into the energy function which could maintain a desired shape of the level set function and keep a signed distance profile near the zero level set. The experimental results on real multi-temporal remote sensing images demonstrate the advantages of our method in terms of human visual perception and segmentation accuracy.
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References
Ayed IB, Mitiche A (2008) A region prior for variational level set image segmentation. IEEE Trans Image Process 17(12):2301–2311
Bazi Y, Melganiand F, Al-Sharari HD (2010) Unsupervised change detection in multispectral remotely sensed imagery with level set methods. IEEE Trans Geosci Remote Sens 48(8):3178–3187
Bruzzone L, Prieto DF (2000) Automatic analysis of the difference image for unsupervised change detection. IEEE Trans Geosci Remote Sens 38(3):1171–1182
Bzai Y, Bruzzone L, Melgani F (2005) An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images. IEEE Trans Geosci Remote Sens 43(4):874–887
Camps-Valls G, Gómez-Chova L, Muñoz-Marí J, Rojo-Álvarez JL, Martínez-Ramón M (2008) Kernel-based framework for multi-temporal and multisource remote sensing data classification and changed detection. IEEE Trans Geosci Remote Sens 46(6):1822–1835
Celik T, Ma K-K (2010) Unsupervised change detection for satellite images using dual-tree complex wavelet transform. IEEE Trans Geosci Remote Sens 48(3):1199–1210
Chan T, Vese L (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277
Chaturvedi I, Ong Y-S, Arumugam RV (2015) Deep transfer learning for classification of time-delayed Gaussian networks. Signal Process 110:250–262
Coppin P, Jonckheere I, Nackaerts K, Muys B, Lambin E (2004) Digital change detection methods in ecosystem monitoring: a review. Int J Remote Sens 25(9):1565–1596
Evans L (1998) Partial differential equations. Amer. Math. Soc., Providence
Gong MG, Zhou ZQ, Ma JJ (2012) Change detection in synthetic aperture radar images based on wavelet fusion and improved fuzzy clustering. IEEE Trans Image Process 21(4):2141–2151
Gong M, Su L, Jia M, Chen W (2014) Fuzzy clustering with a modified MRF energy function for change detection in synthetic aperture radar images. IEEE Trans Fuzzy Syst 22(1):98–109
Gong M, Zhao J, Liu J, Miao Q, Jiao L (2015) Change detection in synthetic aperture radar images based on deep neural networks. IEEE Trans Neural Network Learning Systems 27(1):125–138
Karantzalos K, Paragios N (2009) Recognition-driven two-dimensional competing priors toward automatic and accurate building detection. IEEE Trans Geosci Remote Sens 47(1):133–144
Li C, Xu C, Gui C, Fox MD (2005) Level set evolution without re-initialization: a new variational formulation. Proc IEEE Conf Comput Vis Pattern Recognit 1:430–436
Li C, Xu C, Gui C, Fox MD (2010) Distance regularized level set evolution and its application to image segmentation. IEEE Trans Image Process 19(12):430–436
Li C, Huang R, Ding Z, Gatenby JC, Metaxas DN, Gore JC (2011) A level set method for image segmentation in the presence of intensity Inhomogeneities with application to MRI. IEEE Trans Image Process 20(7):2007–2016
Li H, Gong M, Wang Q, Liu J, Su L (2015) A multiobjective fuzzy clustering method for change detection in SAR images. Appl Soft Comput 46:767–777
Liu YG, Yu YZ (2012) Interactive image segmentation based on level sets of probabilities. IEEE Trans Vis Comput Graph 18(2):202–213
Liu Z-G, Mercier G, Dezert J, Pan Q (2014) Change detection in heterogeneous remote sensing images based on multidimensional evidential reasoning. IEEE Geosci Remote Sens Lett 11(1):168–172
Ma H, Yang Y (2009) Two specific multiple level-set models for high resolution image classification. IEEE Geosci Remote Sens Lett 6(3):558–561
Moser G, Angiati E, Serpico SB (2011) Multiscale unsupervised change detection on optical image by Markov random fields and wavelets. IEEE Geosci Remote Sens Lett 8(4):725–729
Nguyen TNA, Cai JF, Zhang TY, Zheng JM (2012) Robust interactive image segmentation using convex active contours. IEEE Trans Image Process 21(8):3734–3743
Osher S, Fedkiw R (2002) Level set methods and dynamic implicit surfaces. Springer-Verlag, New York
Osher S, Sethian JA (1988) Fronts propagating with curvature-dependent speed: algorithms based on Hamilton–Jacobi formulation. J ComputPhys 79(1):12–49
Prendes J, Chabert M, Pascal F, Giros A, Tourneret JY (2015) A new multivariate statistical model for change detection in images acquired by homogeneous and heterogeneous sensors. IEEE Trans Image Process 24(3):799–812
Shi Y, Karl WC (2008) A real time algorithm for the approximation of the level-set based curve evolution. IEEE Trans Image Process 17(5):645–656
Shi J, Lei Y, Zhou Y, Gong MG (2015) An enhanced hybrid C-means algorithm with strict rough set properties. Appl Soft Comput 46:827–850
Yetgin Z (2012) Unsupervesed change detection of satellite images using local gradual descent. IEEE Geosci Remote Sens 50(5):1919–1929
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This work was supported by the National Natural Science Foundation of China (Grant No. 61603299 and 61602385).
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Lei, Y., Shi, J. & Wu, J. Region-driven distance regularized level set evolution for change detection in remote sensing images. Multimed Tools Appl 76, 24707–24722 (2017). https://doi.org/10.1007/s11042-017-4650-9
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DOI: https://doi.org/10.1007/s11042-017-4650-9