Liu, X., Jiancheng , L., Sahli, H., Yu , M., & Qingqing , H. (2016). Improving unsupervised flood detection with spatio-temporal context on HJ-1B CCD data. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016 (pp. 4402-4405). IEEE.

@inproceedings{9f77e7d4d8244f92aed16877a5b28c35,
title = "Improving unsupervised flood detection with spatio-temporal context on HJ-1B CCD data",
abstract = "The study of flood detection is significant to human life and social economy. In this paper, a completely unsupervised flood detection approach is presented, which combines spatio-temporal context and histogram thresholding. A global thresholding algorithm can be used in most of the cases to distinguish flood from non-flood pixels, but it may not distinguish local grey-level changes when the method is unsupervised. In this work, we introduce a kind of local context information to improve the results. A statistical model is used to establish the spatial relationships between each pixel and its surrounding regions, then a confidence map is computed. If the context structure changes significantly, the pixel is then considered potentially abnormal. Experimental investigations performed on HJ-1B CCD data from Northeast China during large-scale flooding in August 2013 showed higher precision of the proposed approach.",
keywords = "remote sensing",
author = "Xiaoyi Liu and Li Jiancheng and Hichem Sahli and Meng Yu and Huang Qingqing",
year = "2016",
language = "English",
pages = "4402--4405",
booktitle = "IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016",
publisher = "IEEE",
note = "IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 ; Conference date: 10-07-2016 Through 15-07-2016",
url = "http://www.igarss2016.org/",
}