This paper proposes a novel feature-level fusion approach for fire area change detection at a fine level. Two features, including a normalized burn ratio-SWIR (NBRSWIR) fire index based on Landsat-8 OLI SWIR data and the brightness temperature (BT) based on Landsat-8 TIRS data, are combined by using the gradient transfer fusion (GTF) algorithm and a change detection technique to generate a fine fire change map. A real Landsat-8 data set covering a complex fire disaster scenario was utilized to test the performance of the proposed approach. Experimental results demonstrated the effectiveness of the proposed feature-level fusion approach comparing with the reference methods in term of higher separability value and detection accuracy.
|Citation:||Liu, S.; Zheng, Y.; Dalponte, M.; Tong, X. (2019). Feature-level fusion of landsat-8 oli-swir and tirs images for fine burned area change detection. In: IGARSS 2019 - IEEE International Geoscience and Remote Sensing Symposium, Jokohama, Japan, July 28-August 2, 2019. handle: http://hdl.handle.net/10449/57770|
|Organization unit:||Department of Sustainable Agro-ecosystems and Bioresources # CRI|
|Authors:||Liu, S.; Zheng, Y.; Dalponte, M.; Tong, X.|
|Title:||Feature-level fusion of landsat-8 oli-swir and tirs images for fine burned area change detection|
|Scientific Disciplinary Area:||Settore AGR/05 - Assestamento Forestale E Selvicoltura|
|Nature of content:||Contributo in Atti di convegno/Conference paper|
|Appears in Collections:||03 - Conference object|