@inproceedings{2f25a8deb4ac4d8cbd9f5604b0887228,
title = "Unsupervised Detection for Burned Area with Fuzzy C-Means and D-S Evidence Theory",
abstract = "Mapping the burned area of forest fires can contribute significantly to the understanding, quantification, and evaluation of forest fire severity and its impacts on the forest ecosystem. In this paper, an unsupervised detection for burned region based on the Fuzzy C-Means (FCM) and Dempster-Shafer (D-S) evidence theory with the bi-temporal images is proposed. Specifically, according to difference maps from the delta normalized burn ratio and spectral angle index, the Expectation-Maximization (EM) algorithm is used to separate the study area into the definitely burned region and indefinitely burned region. Then, under the enlightenment of the multi-source information fusion theory, the indefinite region is discriminated against further with FCM and D-S evidence theory. Finally, the final fire-burned map can be inferred from the results obtained from the aforementioned steps. The experimental results on two forest fires with bi-temporal Landsat-8 images have shown the potential of the proposed burned area mapping method, in the field of detecting the forest landscape change based on multispectral remote sensing images.",
keywords = "burned area mapping, change detection, D-S evidence theory, forest fire, fuzzy c-means",
author = "Guangyi Wang and Youmin Zhang and Xie, {Wen Fang} and Yaohong Qu",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2nd International Conference on Industrial Artificial Intelligence, IAI 2020 ; Conference date: 23-10-2020 Through 25-10-2020",
year = "2020",
month = oct,
day = "23",
doi = "10.1109/IAI50351.2020.9262167",
language = "英语",
series = "2nd International Conference on Industrial Artificial Intelligence, IAI 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2nd International Conference on Industrial Artificial Intelligence, IAI 2020",
}