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.
Конец ознакомительного фрагмента.
Текст предоставлен ООО «ЛитРес».
Прочитайте эту книгу целиком, купив полную легальную версию на ЛитРес.
Безопасно оплатить книгу можно банковской картой Visa, MasterCard, Maestro, со счета мобильного телефона, с платежного терминала, в салоне МТС или Связной, через PayPal, WebMoney, Яндекс.Деньги, QIWI Кошелек, бонусными картами или другим удобным Вам способом.