938
Views
0
CrossRef citations to date
0
Altmetric
Original Articles

Level set incorporated with an improved MRF model for unsupervised change detection for satellite images

, , , &
Pages 202-210 | Received 14 Mar 2017, Accepted 15 Mar 2017, Published online: 11 Apr 2017

References

  • Ardila, J.P., Bijker, W., Tolpekin, V.A., & Stein, A. (2012). Multitemporal change detection of urban trees using localized region-based active contours in VHR images. Remote Sensing of Environment, 124, 413–210. doi:10.1016/j.rse.2012.05.027
  • Bazi, Y., Melgani, F., & Al-Sharari, H.D. (2010). Unsupervised change detection in multispectral remotely sensed imagery with level set methods. IEEE Transactions on Geoscience and Remote Sensing, 48, 3178–3187. doi:10.1109/TGRS.2010.2045506
  • Bovolo, F., Bruzzone, L., & Marconcini, M. (2008). A novel approach to unsupervised change detection based on a semisupervised SVM and a similarity measure. IEEE Transactions on Geoscience and Remote Sensing, 46, 2070–2082. doi:10.1109/TGRS.2008.916643
  • Brox, T., & Weickert, J. (2006). Level set segmentation with multiple regions. IEEE Transactions on Image Processing, 15, 3213–3218. doi:10.1109/TIP.2006.877481
  • Bruzzone, L., & Prieto, D.F. (2000). Automatic analysis of the difference image for unsupervised change detection. IEEE Transactions on Geoscience and Remote Sensing, 38, 1171–1182. doi:10.1109/36.843009
  • Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 679–698. doi:10.1109/TPAMI.1986.4767851
  • Celik, T. (2009). Unsupervised change detection in satellite images using principal component analysis and k-means clustering. IEEE Geoscience and Remote Sensing Letters, 6, 772–776. doi:10.1109/LGRS.2009.2025059
  • Celik, T., & Ma, -K.-K. (2011). Multitemporal image change detection using undecimated discrete wavelet transform and active contours. IEEE Transactions on Geoscience and Remote Sensing, 49, 706–716. doi:10.1109/TGRS.2010.2066979
  • Chan, T.F., & Vese, L.A. (2001). Active contours without edges. IEEE Transactions on Image Processing, 10, 266–277. doi:10.1109/83.902291
  • Ghosh, A., Mishra, N.S., & Ghosh, S. (2011). Fuzzy clustering algorithms for unsupervised change detection in remote sensing images. Information Sciences, 181, 699–715. doi:10.1016/j.ins.2010.10.016
  • Hao, M., Shi, W., Zhang, H., & Li, C. (2014). Unsupervised change detection with expectation-maximization-based level set. IEEE Geoscience and Remote Sensing Letters, 11, 210–214. doi:10.1109/LGRS.2013.2252879
  • Hussain, M., Chen, D., Cheng, A., Wei, H., & Stanley, D. (2013). Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 80, 91–106. doi:10.1016/j.isprsjprs.2013.03.006
  • Kusetogullari, H., Yavariabdi, A., & Celik, T. (2015). Unsupervised change detection in multitemporal multispectral satellite images using parallel particle swarm optimization. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 2151–2164. doi:10.1109/JSTARS.2015.2427274
  • Li, H., Gong, M., & Liu, J. (2015). A local statistical fuzzy active contour model for change detection. IEEE Geoscience and Remote Sensing Letters, 12, 582–586. doi:10.1109/LGRS.2014.2352264
  • Li, Z., Shi, W., Myint, S.W., Lu, P., & Wang, Q. (2016). Semi-automated landslide inventory mapping from bitemporal aerial photographs using change detection and level set method. Remote Sensing of Environment, 175, 215–230. doi:10.1016/j.rse.2016.01.003
  • Lu, D., Mausel, P., Brondízio, E., & Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25, 2365–2401. doi:10.1080/0143116031000139863
  • Mumford, D., & Shah, J. (1989). Optimal approximations by piecewise smooth functions and associated variational problems. Communications on Pure and Applied Mathematics, 42, 577–685. doi:10.1002/(ISSN)1097-0312
  • Osher, S., & Sethian, J.A. (1988). Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics, 79, 12–49. doi:10.1016/0021-9991(88)90002-2
  • Shi, J., Wu, J., Paul, A., Jiao, L., & Gong, M. (2014). Change detection in synthetic aperture radar images based on fuzzy active contour models and genetic algorithms. Mathematical Problems in Engineering, 2014,15.
  • Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., … Rother, C. (2008). A comparative study of energy minimization methods for Markov random fields with smoothness-based priors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30, 1068–1080. doi:10.1109/TPAMI.2007.70844
  • Tso, B., & Olsen, R.C. (2005). A contextual classification scheme based on MRF model with improved parameter estimation and multiscale fuzzy line process. Remote Sensing of Environment, 97, 127–136. doi:10.1016/j.rse.2005.04.021
  • Wang, F., Wu, Y., Zhang, Q., Zhang, P., Li, M., & Lu, Y. (2013). Unsupervised change detection on SAR images using triplet Markov field model. IEEE Geoscience and Remote Sensing Letters, 10, 697–701. doi:10.1109/LGRS.2012.2219494
  • Yetgin, Z. (2012). Unsupervised change detection of satellite images using local gradual descent. IEEE Transactions on Geoscience and Remote Sensing, 50, 1919–1929. doi:10.1109/TGRS.2011.2168230