Spectral unmixing for fire smoke detection and removal

Optical remote sensing images are often contaminated by smoke from forest fires or biomass burning. In this paper, a smoke removal method is proposed based on a spectral unmixing technique. Smoke components are detected first by generating subpixel smoke fraction masks with spectral mixture analysis. The smoke component is then subtracted from each smoky pixel. Finally, the attenuated signal is restored by rescaling the abundances of the endmember classes present in each pixel. The proposed method has high feasibility and is not dependent on other auxiliary data. The experiments have been conducted on an AVIRIS data set and the results show that the proposed method is effective for smoke detection and data correction.