基于GF-4 PMI数据的亮温差校正火点检测方法研究  

Research on Fire Point Monitoring Based on GaoFen-4 Satellite Data With Bright Temperature Difference Correction

作　　者：王尧 王世新 周艺 王福涛 王振庆 WANG Yao;WANG Shi-xin;ZHOU Yi;WANG Fu-tao;WANG Zhen-qing(Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China;University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院空天信息创新研究院,北京100094 [2]中国科学院大学,北京100049

出　　处：《光谱学与光谱分析》2021年第11期3595-3601,共7页Spectroscopy and Spectral Analysis

基　　金：国家重点研发计划专项课题(2017YF080504101,2016YFC0803004)资助。

摘　　要：高分四号PMI可为防灾减灾提供稳定数据,其搭载的中红外传感器可以很好地应用于快速火灾监测中。但由于缺少传统火灾监测的热红外波段,高分四号提供的光谱信息大多作为灾中监测的辅助数据,且现有的火点识别研究所构建的火点自适应阈值检测算法受单一波段的影响,错检率和漏检率均偏高。为进一步探究高分四号数据在林火监测中的应用方法,提高火点识别精度,本研究分析高分四号数据的特点,结合单通道红外光谱的火点监测方法,应用上下文思想提出一种基于双时相影像的亮温差校正火点检测的方法来进一步提高检测精度。该方法使用灾前和灾中两期影像,具体分为时间尺度上基于空间插值的亮温补偿获取,空间尺度上的上下文自适应阈值分割以及火点判识三个部分。首先将两期影像做差值处理,并将潜在火点周围动态邻域内其他无污染像元的亮温差作为采样点进行空间插值,随后将插值结果带入灾前影像中得到灾中未发生火灾时的背景亮温,最后利用判别条件进行火点判别和虚警剔除,得到最终火点检测结果。其中在灾中背景亮温的预测研究对比了反距离加权插值(inverse distance weigh)、简单克里金插值(simple kriging)和普通克里金插值(ordinary kriging)三种插值方法,从拟合结果来看普通克里金插值既体现了像素区域的波动性又有一定的平滑效果避免峰值过高,是较为理想的拟合结果。实验以目视解译的火点数据为参照验证了山西沁源县和内蒙古呼伦贝尔新巴尔虎左旗地区的两起火灾,对比最新提出的单时相火点检测算法,研究结果表明引进的亮温差校正数据可以更好地拟合背景亮温,减少错分误差至3%,并保持综合评价指标Fβ分数在0.9以上。该方法有效结合了高分四号空间和时间的信息,未来可用于高分四号PMI数据自动化火点检测与快速提取。GF-4 can provide stable data for disaster prevention and mitigation, and its mid-infrared sensor can be well applied in rapid-fire monitoring. Because of lacking far-infrared, the spectral information that GF-4 provided is supplementary data as usual. Affected by a single band, the commission error and omission error of the adaptive threshold method is high. Therefore, to probe the potential of GF-4 data and improve the accuracy of fire point recognition, this study analyzed the characteristics of GF-4 data and proposed a fire point detection method with brightness temperature difference correction based on dual temporal image. The method mainly includes three parts: brightness temperature compensation acquisition based on Kriging interpolation on temporal scale, adaptive threshold segmentation on a spatial scale based on contextual information, and fire point detection, with two images-before and during the fire event. Firstly, the difference between the two images is processed. Moreover, we use this difference of non-polluted pixels in the dynamic neighborhood around the potential fire point as the sampling data for spatial interpolation and then substitute the result of the previous step into the first image. Finally, using discrimination conditions for fire point discrimination and false alarm elimination get the final results. The study also compares three spatial interpolation acquisitions: Inverse Distance Weigh, Simple Kriging and Ordinary Kriging. From the fitting results, the Ordinary Kriging can reflect the volatility of the pixel area and has a certain smoothing effect to avoid peaks of background brightness temperature, which is the better method. The study area contains two fires in Qinyuan, Shanxi Province and New Barhu Right Bannerin, Inner Mongolia. Results show that compared with the traditional single time phase algorithm, introducing brightness temperature difference correction data can better fit the background brightness temperature, reducing the commission error to 3% and obtaining comprehe

关 键 词：高分四号 火点检测 温度补偿 克里金 

分 类 号：S762.2[农业科学—森林保护学]