Analysis of Sensitive Spectral Bands for Burning Status Detection Using Hyper-Spectral Images of Tiangong-01
QIN Xian-lin1, ZHU Xi1, YANG Fei1, 2, ZHAO Kai-rui1, 3, PANG Yong1, LI Zeng-yuan1, LI Xu-zhi4, ZHANG Jiu-xing4
1. Research Institute of Forest Resources Information Technique, Chinese Academy of Forestry; Key Laboratory of Forestry Remote Sensing and Information Techniques, State Forestry Administration, Beijing 100091, China 2. Communication University of China, Beijing 100024, China 3. Southwest Forestry University, Kunming 650224, China 4. Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing 100094, China
Abstract：To obtain the sensitive spectral bands for detection of information on 4 kinds of burning status, i.e. flaming, smoldering, smoke, and fire scar, with satellite data, analysis was conducted to identify suitable satellite spectral bands for detection of information on these 4 kinds of burning status by using hyper-spectrum images of Tiangong-01(TG-01) and employing a method combining statistics and spectral analysis. The results show that: in the hyper-spectral images of TG-01, the spectral bands differ obviously for detection of these 4 kinds of burning status; in all hyper-spectral short-wave infrared channels, the reflectance of flaming is higher than that of all other 3 kinds of burning status, and the reflectance of smoke is the lowest; the reflectance of smoke is higher than that of all other 3 kinds of burning status in the channels corresponding to hyper-spectral visible near-infrared and panchromatic sensors. For spectral band selection, more suitable spectral bands for flaming detection are 1 000.0～1 956.0 and 2 020.0～2 400.0 nm; the suitable spectral bands for identifying smoldering are 930.0～1 000.0 and 1 084.0～2 400.0 nm; the suitable spectral bands for smoke detection is in 400.0～920.0 nm; for fire scar detection, it is suitable to select bands with central wavelengths of 900.0～930.0 and 1 300.0～2 400.0 nm, and then to combine them to construct a detection model.
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