Unsupervised learning of probabilistic subspaces for multi-spectral and multi-temporal image-based disaster mapping
Tóm tắt
Accurate and timely identification of regions damaged by a natural disaster is critical for assessing the damages and reducing the human life cost. The increasing availability of satellite imagery and other remote sensing data has triggered research activities on development of algorithms for detection and monitoring of natural events. Here, we introduce an unsupervised subspace learning-based methodology that uses multi-temporal and multi-spectral satellite images to identify regions damaged by natural disasters. It first performs region delineation, matching, and fusion. Next, it applies subspace learning in the joint regional space to produce a change map. It identifies the damaged regions by estimating probabilistic subspace distances and rejecting the non-disaster changes. We evaluated the performance of our method on seven disaster datasets including four wildfire events, two flooding events, and a earthquake/tsunami event. We validated our results by calculating the dice similarity coefficient (DSC), and accuracy of classification between our disaster maps and ground-truth data. Our method produced average DSC values of 0.833 and 0.736, for wildfires and floods, respectively, and overall DSC of 0.855 for the tsunami event. The evaluation results support the applicability of our method to multiple types of natural disasters.
Tài liệu tham khảo
Voigt, S., Kemper, T., Riedlinger, T., Kiefl, R., Scholte, K., Mehl, H.: Satellite image analysis for disaster and crisis-management support. IEEE Trans. Geosci. Remote Sens. 45, 1520–1528 (2007). https://doi.org/10.1109/TGRS.2007.895830
Mori, N., Takahashi, T., Yasuda, T., Yanagisawa, H.: Survey of 2011 Tohoku earthquake tsunami inundation and run-up. Geophys. Res. Lett. 38(7) (2011)
Joyce, K.E., Belliss, S.E., Samsonov, S.V., McNeill, S.J., Glassey, P.J.: A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters. Prog. Phys. Geogr. 33(2), 183–207 (2009)
Singh, A.: Review article digital change detection techniques using remotely-sensed data. Int. J. Remote Sens. 10(6), 989–1003 (1989)
Chen, J., Yuan, Z., Peng, J., Chen, L., Huang, H., Zhu, J., Liu, Y., Li, H.: Dasnet: dual attentive fully convolutional siamese networks for change detection in high-resolution satellite images. IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 14, 1194–1206 (2020)
Zhang, H., Lin, M., Yang, G., Zhang, L.: ESCNet: an end-to-end superpixel-enhanced change detection network for very-high resolution remote sensing images. IEEE Trans. Neural Netw. Learn. Syst. 34(1), 28–42 (2023). https://doi.org/10.1109/TNNLS.2021.3089332
Ma, Y., Chen, F., Liu, J., He, Y., Duan, J., Li, X.: An automatic procedure for early disaster change mapping based on optical remote sensing. Remote Sens. 8(4), 272 (2016)
Sublime, J., Kalinicheva, E.: Automatic post-disaster damage mapping using deep-learning techniques for change detection: case study of the tohoku tsunami. Remote Sens. 11(9), 1123 (2019)
Li, H., Celik, T., Longbotham, N., Emery, W.J.: Gabor feature based unsupervised change detection of multitemporal SAR images based on two-level clustering. IEEE Geosci. Remote Sens. Lett. 12(12), 2458–2462 (2015). https://doi.org/10.1109/LGRS.2015.2484220
Navarro, G., Caballero, I., Silva, G., Parra, P.-C., Vázquez, Á., Caldeira, R.: Evaluation of forest fire on madeira island using sentinel-2a msi imagery. Int. J. Appl. Earth Obs. Geoinform. 58, 97–106 (2017)
Soltani, K., Ebtehaj, I., Amiri, A., Azari, A., Gharabaghi, B., Bonakdari, H.: Mapping the spatial and temporal variability of flood susceptibility using remotely sensed normalized difference vegetation index and the forecasted changes in the future. Sci. Total Environ. 770, 145288 (2021)
Wang, Z., Liu, J., Li, J., Zhang, D.D.: Multi-spectral water index (muwi): a native 10-m multi-spectral water index for accurate water mapping on sentinel-2. Remote Sens. 10(10), 1643 (2018)
Cocke, A.E., Fulé, P.Z., Crouse, J.E.: Comparison of burn severity assessments using differenced normalized burn ratio and ground data. Int. J. Wildland Fire 14(2), 189–198 (2005)
Quintano, C., Fernández-Manso, A., Fernández-Manso, O.: Combination of landsat and sentinel-2 msi data for initial assessing of burn severity. Int. J. Appl. Earth Obs. Geoinform. 64, 221–225 (2018)
Torres, R., Mouginis-Mark, P., Self, S., Garbeil, H., Kallianpur, K., Quiambao, R.: Monitoring the evolution of the pasig-potrero alluvial fan, pinatubo volcano, using a decade of remote sensing data. J. Volcanol. Geoth. Res. 138(3–4), 371–392 (2004)
Khandelwal, P., Singh, K.K., Singh, B., Mehrotra, A.: Unsupervised change detection of multispectral images using wavelet fusion and kohonen clustering network. Int. J. Eng. Technol. 5(2), 1401–1406 (2013)
Celik, T.: Unsupervised change detection in satellite images using principal component analysis and \(k\)-means clustering. IEEE Geosci. Remote Sens. Lett. 6, 772–776 (2009). https://doi.org/10.1109/LGRS.2009.2025059
Celik, T., Curtis, C.V.: Resolution selective change detection in satellite images. In: 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 970–973. IEEE (2010). https://doi.org/10.1109/ICASSP.2010.5495301
Li, S., Fang, L., Yin, H.: Multitemporal image change detection using a detail-enhancing approach with nonsubsampled contourlet transform. IEEE Geosci. Remote Sens. Lett. 9, 836–840 (2012). https://doi.org/10.1109/LGRS.2011.2182632
Yetgin, Z.: Unsupervised change detection of satellite images using local gradual descent. IEEE Trans. Geosci. Remote Sens. 50, 1919–1929 (2012). https://doi.org/10.1109/TGRS.2011.2168230
Gupta, N., Ari, S., Panigrahi, N.: Change detection in landsat images using unsupervised learning and RBF-based clustering. IEEE Trans. Emerg. Topics Comput. Intell. (2019). https://doi.org/10.1109/TETCI.2019.2932087
Gong, M., Su, L., Jia, M., Chen, W.: Fuzzy clustering with a modified MRF energy function for change detection in synthetic aperture radar images. IEEE Trans. Fuzzy Syst. 22, 98–109 (2014). https://doi.org/10.1109/TFUZZ.2013.2249072
Wang, S., Liu, X., Zhu, X., Zhang, P., Zhang, Y., Gao, F., Zhu, E.: Fast parameter-free multi-view subspace clustering with consensus anchor guidance. IEEE Trans. Image Process. 31, 556–568 (2021)
Xu, Y., Xiang, S., Huo, C., Pan, C.: Change detection based on auto-encoder model for VHR images. In: MIPPR 2013: Pattern Recognition and Computer Vision, vol. 8919, p. 891902 (2013). https://doi.org/10.1117/12.2031104. International Society for Optics and Photonics
Bai, Y., Mas, E., Koshimura, S.: Towards operational satellite-based damage-mapping using u-net convolutional network: a case study of 2011 tohoku earthquake-tsunami. Remote Sens. 10(10), 1626 (2018). https://doi.org/10.3390/rs10101626
Lei, T., Zhang, Q., Xue, D., Chen, T., Meng, H., Nandi, A.K.: End-to-end change detection using a symmetric fully convolutional network for landslide mapping. In: ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3027–3031. IEEE (2019). https://doi.org/10.1109/icassp.2019.8682802
Moghaddam, B.: Principal manifolds and probabilistic subspaces for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 24(6), 780–788 (2002)
Rifai, S., Dauphin, Y.N., Vincent, P., Bengio, Y., Muller, X.: The manifold tangent classifier. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc. (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/d1f44e2f09dc172978a4d3151d11d63e-Paper.pdf
Yan, C., Gong, B., Wei, Y., Gao, Y.: Deep multi-view enhancement hashing for image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 43(4), 1445–1451 (2020)
Yan, C., Meng, L., Li, L., Zhang, J., Wang, Z., Yin, J., Zhang, J., Sun, Y., Zheng, B.: Age-invariant face recognition by multi-feature fusionand decomposition with self-attention. ACM Trans. Multimed. Comput. Commun. Appl. (TOMM) 18(1s), 1–18 (2022)
Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)
Makrogiannis, S., Vanhamel, I., Fotopoulos, S., Sahli, H., Cornelis, J.P.: Watershed-based multiscale segmentation method for color images using automated scale selection. J. Electron. Imaging 14(3), 033007 (2005)
Okorie, A., Makrogiannis, S.: Region-based image registration for remote sensing imagery. Comput. Vis. Image Underst. 189, 102825 (2019)
Okorie, A.M., Makrogiannis, S.: Subspace analysis for multi-temporal disaster mapping using satellite imagery. In: Bebis, G., Li, B., Yao, A., Liu, Y., Duan, Y., Lau, M., Khadka, R., Crisan, A., Chang, R. (eds.) Advances in Visual Computing, pp. 162–173. Springer, Cham (2022)
Simard, P.Y., LeCun, Y.A., Denker, J.S., Victorri, B.: In: Montavon, G., Orr, G.B., Müller, K.-R. (eds.) Transformation Invariance in Pattern Recognition—Tangent Distance and Tangent Propagation, pp. 235–269. Springer, Berlin, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35289-8_17
Fitzgibbon, A.W., Zisserman, A.: Joint manifold distance: a new approach to appearance based clustering. In: 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., vol. 1. IEEE (2003)
Chang, J.-M., Kirby, M.: Face recognition under varying viewing conditions with subspace distance. In: Proc. Int’l Conf. on Artificial Intelligence and Pattern Recognition (AIPR-09), Orlando, FL, pp. 16–23 (2009)
Broomhead, D., Jones, R., King, G.P.: Topological dimension and local coordinates from time series data. J. Phys. A Math. Gen. 20(9), 563 (1987)
Zhu, Z., Woodcock, C.E.: Object-based cloud and cloud shadow detection in landsat imagery. Remote Sens. Environ. 118, 83–94 (2012)
Zhu, Z., Wang, S., Woodcock, C.E.: Improvement and expansion of the fmask algorithm: cloud, cloud shadow, and snow detection for landsats 4–7, 8, and sentinel 2 images. Remote Sens. Environ. 159, 269–277 (2015)