Feature-Level Fusion of Landsat-8 OLI-SWIR and TIR Images for Fine Burned Area Change Detection

2019 
This paper proposes a novel feature-level fusion approach for burned area change detection at a fine level. The proposed approach relies on two features. The first feature is a modified normalized burn ratio (MNBR) fire index based on Landsat-8 OLI SWIR data, and the second feature is the Bright temperature (BT) based on Landsat-8 TIR data. Then two features are combined by using the gradient transfer fusion algorithm and a change detection technique to generate a fine burned area change map. A real Landsat-8 data set covering a complex fire disaster scenario is utilized to test the performance of the proposed approach. Experimental results demonstrate the effectiveness of the proposed feature-level fusion approach comparing with the reference methods in term of higher separability value and detection accuracy.
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