红外与激光工程, 2018, 47 (11): 1104004, 网络出版: 2019-01-10  

基于遥感数据的地球背景中红外场景仿真

Simulation of global mid-infrared background based on remote sensing data
作者单位
1 中国科学院安徽光学精密机械研究所 中国科学院环境光学与技术重点实验室, 安徽 合肥 230031
2 中国科学技术大学, 安徽 合肥 230026
3 光学辐射重点实验室, 北京 100854
摘要
卫星遥感是研究地球大气背景红外辐射特性的重要手段, 受大气影响及传感器观测条件的限制, 无法获取多气象、多探测条件的辐射数据。针对该问题, 基于JHU地物光谱数据库, 分析了植被、水体、岩石等光谱辐射特性, 结合传感器光谱响应, 建立了3~5 μm基于光谱相关性的地表辐射波段转换模型, 并利用逐步回归法计算误差, 模型误差小于10%。利用MODIS、AIRS等多源遥感数据产品, 根据地表-大气-传感器辐射传输模型, 模拟不同时空分辨率、探测条件等的地球背景辐射中红外图像。结果表明, 利用多源遥感数据进行中红外图像仿真, 可以实现大尺度、具有细致纹理结构的图像模拟, 应用于遥感研究。
Abstract
Satellite remote sensing is an important means to study the infrared radiation characteristics of the earth's atmosphere background. Due to the influence of atmosphere and the limitation of sensor observation conditions, it is impossible to obtain multi-meteorological and multi-detection radiation data. Aiming at this problem, the spectral radiation characteristics of vegetation, water and rock were analyzed based on JHU spectral database. Based on the spectral response of sensors, a model of surface radiation band transformation model based on spectral correlation was established for 3-5 μm. The error was calculated by the stepwise regression method, and the model error was less than 10%. Using MODIS, AIRS and other multi-source remote sensing products, and according to the surface-atmosphere-sensor radiation transfer model, the mid-infrared image of the earth background radiation was simulated. It can simulate the mid-infrared image of the earth background radiation with different temporal and spatial resolution and detection conditions. The results show that the simulation of mid-infrared image with multi-source remote sensing data can achieve large-scale and fine texture image simulation which can be used in remote sensing research.

李霞, 刘建国, 董雁冰, 刘兴润. 基于遥感数据的地球背景中红外场景仿真[J]. 红外与激光工程, 2018, 47(11): 1104004. Li Xia, Liu Jianguo, Dong Yanbing, Liu Xingrun. Simulation of global mid-infrared background based on remote sensing data[J]. Infrared and Laser Engineering, 2018, 47(11): 1104004.

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