Change Detection and Image Time-Series Analysis 1. Группа авторов

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СКАЧАТЬ by: (a) IR-MAD; (b) S²CVA; (c) proposed M²C²VA([u, v] = [1, 6]; (d) proposed SPC²VA with N = 35,140 and m = 30, respectively, where the first row is the whole image scene, and the second and third rows represent two subset results for a detailed visual comparison purpose (Indonesia tsunami dataset). For a color version of this figure, see www.iste.co.uk/atto/change1.zip

1.6. Conclusion

Owing to the automatic and unsupervised nature, unsupervised CD always represents a very interesting and important CD research and application frontier. However, the absence of ground reference and prior knowledge makes this practical problem more challenging and complex than the supervised ones. In this chapter, we reviewed the current development of unsupervised CD methods in multitemporal multispectral remote sensing images, and analyzed existing open issues and challenges. In particular, we focused on the spectral–spatial perspective to find robust solutions to the important multiclass CD problem. Accordingly, two approaches were proposed, including M²C²VA and SPC²VA. By taking advantage of the spectral and spatial joint analysis on the multispectral change representation, the original pixel-level CD performance was enhanced by considering both the spectral variation at the global scale and the spectral homogeneity and spatial connectivity and regularity of change targets at the local scale. Experimental results obtained two real multispectral datasets covering a complex urban scenario, and a large-scale tsunami disaster scenario confirmed the effectiveness of the proposed approaches in terms of higher CD accuracy and computational efficiency when compared with the reference methods. For future works, advanced techniques still need to be designed to deal with more complex real unsupervised CD cases, mainly focusing on, but not limited to the open issues and challenges pointed out in section 1.2.2.

1.7. Acknowledgements

      This work was supported by the Natural Science Foundation of China under Grant 42071324, 41601354, and by the Shanghai Rising-Star Program (21QA1409100).

1.8. References

      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.

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