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Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S. (2012). Slic superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11), 2274–2282.

Ban, Y. and Yousif, O. (2016). Change Detection Techniques: A Review. Springer International Publishing, Cham.

Bazi, Y., Bruzzone, L., Melgani, F. (2005). An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images. IEEE Transactions on Geoscience and Remote Sensing, 43(4), 874–887.

Benediktsson, J.A., Palmason, J.A., Sveinsson, J.R. (2005). Classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Transactions on Geoscience and Remote Sensing, 43(3), 480–491.

Bouziani, M., Goïta, K., He, D.-C. (2010). Automatic change detection of buildings in urban environment from very high spatial resolution images using existing geodatabase and prior knowledge. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 143–153 [Online]. Available at: http://www.sciencedirect.com/science/article/pii/S092427160900121X.

Bovolo, F. (2009). A multilevel parcel-based approach to change detection in very high resolution multitemporal images. IEEE Geoscience and Remote Sensing Letters, 6(1), 33–37.

Bovolo, F. and Bruzzone, L. (2007a). A split-based approach to unsupervised change detection in large-size multitemporal images: Application to tsunami-damage assessment. IEEE Transactions on Geoscience and Remote Sensing, 45(6), 1658–1670.

Bovolo, F. and Bruzzone, L. (2007b). A theoretical framework for unsupervised change detection based on change vector analysis in the polar domain. IEEE Transactions on Geoscience and Remote Sensing, 45(1), 218–236.

Bovolo, F. and Bruzzone, L. (2011). An adaptive thresholding approach to multiple-change detection in multispectral images. IEEE International Geoscience and Remote Sensing Symposium, 233–236.

Bovolo, F. and Bruzzone, L. (2015). The time variable in data fusion: A change detection perspective. IEEE Geoscience and Remote Sensing Magazine, 3(3), 8–26.

Bovolo, F., Marchesi, S., Bruzzone, L. (2012). A framework for automatic and unsupervised detection of multiple changes in multitemporal images. IEEE Transactions on Geoscience and Remote Sensing, 50(6), 2196–2212.

Bruzzone, L. and Bovolo, F. (2013). A novel framework for the design of change-detection systems for very-high-resolution remote sensing images. Proceedings of the IEEE, 101(3), 609–630.

Bruzzone, L. and Prieto, D.F. (2000a). Automatic analysis of the difference image for unsupervised change detection. IEEE Transactions on Geoscience and Remote Sensing, 38(3), 1171–1182.

Bruzzone, L. and Prieto, D.F. (2000b). A minimum-cost thresholding technique for unsupervised change detection. International Journal of Remote Sensing, 21(18), 3539–3544 [Online]. Available at: https://doi.org/10.1080/014311600750037552.

Celik, T. (2009). Unsupervised change detection in satellite images using principal component analysis and k-means clustering. IEEE Geoscience and Remote Sensing Letters, 6(4), 772–776.

Celik, T. and Ma, K.K. (2011). Multitemporal image change detection using undecimated discrete wavelet transform and active contours. IEEE Transactions on Geoscience and Remote Sensing, 49(2), 706–716.

Chen, J., Gong, P., He, C., Pu, R., Shi, P. (2003). Land-use/land-cover change detection using improved change-vector analysis. Photogrammetric Engineering and Remote Sensing, 69(4), 369–379.

Chen, G., Hay, G.J., Carvalho, L.M.T., Wulder, M.A. (2012). Object-based change detection. International Journal of Remote Sensing, 33(14), 4434–4457 [Online]. Available at: https://doi.org/10.1080/01431161.2011.648285.

Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., Lambin, E. (2004). Review article digital change detection methods in ecosystem monitoring: A review. International Journal of Remote Sensing, 25(9), 1565–1596 [Online]. Available at: https://doi.org/10.1080/0143116031000101675.

Dalla Mura, M., Benediktsson, J.A., Waske, B., Bruzzone, L. (2010). Morphological attribute profiles for the analysis of very high resolution images. IEEE Transactions on Geoscience and Remote Sensing, 48(10), 3747–3762.

Du, P., Liu, S., Gamba, P., Tan, K., Xia, J. (2012). Fusion of difference images for change detection over urban areas. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(4), 1076–1086.

Du, P., Liu, S., Xia, J., Zhao, Y. (2013). Information fusion techniques for change detection from multi-temporal remote sensing images. Information Fusion, 14(1), 19–27 [Online]. Available at: http://www.sciencedirect.com/science/article/pii/S1566253512000565.

Falco, N., Mura, M.D., Bovolo, F., Benediktsson, J.A., Bruzzone, L. (2013). Change detection in VHR images based on morphological attribute profiles. IEEE Geoscience and Remote Sensing Letters, 10(3), 636–640.

Ghosh, A., Mishra, N.S., Ghosh, S. (2011). Fuzzy clustering algorithms for unsupervised change detection in remote sensing images. Information Sciences, 181(4), 699–715 [Online]. Available at: http://www.sciencedirect.com/science/article/pii/S0020025510005153.

Han, P., Gong, J., Li, Z. (2008). A new approach for choice of optimal spatial scale in image classification based on entropy. Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University, 033(7), 676–679.

Han, Y., Javed, A., Jung, S., Liu, S. (2020). Object-based change detection of very high resolution images by fusing pixel-based change detection results using weighted Dempster–Shafer theory. Remote Sensing, 12(6) [Online]. Available at: https://www.mdpi.com/2072-4292/12/6/983.

Huang, X., Zhang, L., Zhu, T. (2014). Building change detection from multitemporal high-resolution remotely sensed images based on a morphological building index. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(1), 105–115.

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 [Online]. Available at: http://www.sciencedirect.com/science/article/pii/S0924271613000804.

Kaszta, A., Van De Kerchove, R., Ramoelo, A., Cho, M.A., Madonsela, S., Mathieu, R., Wolff, E. (2016). Seasonal separation of African savanna components using WorldView-2 imagery: A comparison of pixel- and object-based approaches and selected classification algorithms. Remote Sensing, 8(9) [Online]. Available at: https://www.mdpi.com/2072-4292/8/9/763.

Keshava, N. (2004). Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries. IEEE Transactions on Geoscience and Remote Sensing, 42(7), 1552–1565.

Khan, S.H., He, X., Porikli, F., Bennamoun, M. (2017). Forest change detection in incomplete satellite images with deep neural networks. IEEE Transactions on Geoscience and Remote Sensing, 55(9), 5407–5423.

Leichtle, T., Geiß, C., Wurm, M., Lakes, T., Taubenböck, H. (2017). Unsupervised change detection in VHR remote sensing imagery – An object-based clustering approach in a dynamic urban environment. International Journal of Applied Earth Observation and Geoinformation, 54, 15–27 [Online]. Available at: http://www.sciencedirect.com/science/article/pii/S0303243416301490.

Li, H., Celik, T., Longbotham, N., Emery, W.J. (2015). Gabor feature based unsupervised change detection of multitemporal SAR images based on two-level clustering. IEEE Geoscience and Remote Sensing Letters, 12(12), 2458–2462.

Liu, S. and Du, P. (2010). Object-oriented change detection from multi-temporal remotely sensed images. Geographic Object-Based Image Analysis, number XXXVIII-4/C7.

Liu, S., Bruzzone, L., Bovolo, F., Du, P. (2012). Unsupervised hierarchical spectral analysis for change detection in hyperspectral images. 4th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), pp. 1–4.

Liu, S., Bruzzone, L., Bovolo, F., Zanetti, M., Du, P. (2015). Sequential spectral change vector analysis for iteratively discovering and detecting multiple changes in hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 53(8), 4363–4378.

Liu, S., Du, Q., Tong, X., Samat, A., Bruzzone, L., Bovolo, F. (2017a). Multiscale morphological compressed change vector analysis for unsupervised multiple change detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(9), 4124–4137.

Liu, S., Tong, X., Bruzzone, L., Du, P. (2017b). A novel semisupervised framework for multiple change detection in hyperspectral images. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 173–176.

Liu, J., Chen, K., Xu, G., Li, H., Yan, M., Diao, W., Sun, X. (2019a). Semi-supervised change detection based on graphs with generative adversarial networks. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 74–77.

Liu, S., Du, Q., Tong, X., Samat, A., Bruzzone, L. (2019b). Unsupervised change detection in multispectral remote sensing images via spectral-spatial band expansion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(9), 3578–3587.

Liu, S., Marinelli, D., Bruzzone, L., Bovolo, F. (2019c). A review of change detection in multitemporal hyperspectral images: Current techniques, applications, and challenges. IEEE Geoscience and Remote Sensing Magazine, 7(2), 140–158.

Liu, S., Hu, Q., Tong, X., Xia, J., Du, Q., Samat, A., Ma, X. (2020a). A multi-scale superpixel-guided filter feature extraction and selection approach for classification of very-high-resolution remotely sensed imagery. Remote Sensing, 12(5) [Online]. Available at: https://www.mdpi.com/2072-4292/12/5/862.

Liu, S., Zheng, Y., Dalponte, M., Tong, X. (2020b). A novel fire index-based burned area change detection approach using Landsat-8 OLI data. European Journal of Remote Sensing, 53(1), 104–112 [Online]. Available at: https://doi.org/10.1080/22797254.2020.1738900.

Lu, D., Mausel, P., Brondízio, E., Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25(12), 2365–2401 [Online]. Available at: https://doi.org/10.1080/0143116031000139863.

Malila, W. (1980). Change vector analysis: An approach for detecting forest changes with landsat. LARS Symposia, Purdue University, West Lafayette, IN.

Mou, L., Bruzzone, L., Zhu, X.X. (2019). Learning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 57(2), 924–935.

Mura, M.D., Benediktsson, J.A., Bovolo, F., Bruzzone, L. (2008). An unsupervised technique based on morphological filters for change detection in very high resolution images. IEEE Geoscience and Remote Sensing Letters, 5(3), 433–437.

Nielsen, A.A. (2007). The regularized iteratively reweighted mad method for change detection in multi- and hyperspectral data. IEEE Transactions on Image Processing, 16(2), 463–478.

Nielsen, A.A. and Canty, M.J. (2008). Kernel principal component analysis for change detection [Online]. Available at: http://www2.compute.dtu.dk/pubdb/pubs/5667-full.html.

Okyay, U., Telling, J., Glennie, C.L., Dietrich, W.E. (2019). Airborne lidar change detection: An overview of earth sciences applications. Earth-Science Reviews, 198, 102929 [Online]. Available at: http://www.sciencedirect.com/science/article/pii/S0012825218306470.

Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66.

Saha, S., Bovolo, F., Bruzzone, L. (2019). Unsupervised deep change vector analysis for multiple-change detection in VHR images. IEEE Transactions on Geoscience and Remote Sensing, 57(6), 3677–3693.

Singh, A. (1989). Review article digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10(6), 989–1003 [Online]. Available at: https://doi.org/10.1080/01431168908903939.

Song, X.P., Hansen, M.C., Stehman, S.V., Potapov, S.V., Tyukavina, A., Vermote, E.F., Townshend, J.R. (2018). Global land change from 1982 to 2016. Nature, 560, 639–643.

Tong, X., Pan, H., Liu, S., Li, B., Luo, X., Xie, H., Xu, X. (2020). A novel approach for hyperspectral change detection based on uncertain area analysis and improved transfer learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 2056–2069.

Wang, X., Liu, S., Du, P., Liang, H., Xia, J., Li, Y. (2018). Object-based change detection in urban areas from high spatial resolution images based on multiple features and ensemble learning. Remote Sensing, 10(2) [Online]. Available at: https://www.mdpi.com/2072-4292/10/2/276.

Wei, C., Zhao, P., Li, X., Wang, Y., Liu, F. (2019). Unsupervised change detection of VHR remote sensing images based on multi-resolution Markov random field in wavelet domain. International Journal of Remote Sensing, 40(20), 7750–7766 [Online]. Available at: https://doi.org/10.1080/01431161.2019.1602792.

Wu, Z., Hu, Z., Fan, Q. (2012). Superpixel-based unsupervised change detection using multi-dimensional change vector analysis and SVM-based classification. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, I-7, 257–262 [Online]. Available at: https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/I-7/257/2012/.

Zanetti, M., Bovolo, F., Bruzzone, L. (2015). Rayleigh–Rice mixture parameter estimation via EM algorithm for change detection in multispectral images. IEEE Transactions on Image Processing, 24(12), 5004–5016.

Zhang, W., Lu, X., Li, X. (2018). A coarse-to-fine semi-supervised change detection for multispectral images. IEEE Transactions on Geoscience and Remote Sensing, 56(6), 3587–3599.