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Analysis of diurnal, long-wave hyperspectral measurements of natural background and manmade targets under different weather conditions

机译:不同天气条件下自然本底和人造目标的昼夜,长波高光谱测量结果分析

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In this paper we describe the end-to-end processing of image Fourier Transform spectrometry data taken at Picatinny Arsenal in New Jersey with the long-wave hyperspectral camera from Telops. The first part of the paper discusses the processing from raw data to calibrated radiance and emissivity data. Data was taken during several months under different weather conditions every 6 minutes from a 213ft high tower of surrogate tank targets for a project sponsored by the Army Research Laboratory in Adelphi, MD. An automatic calibration and analysis program was developed which creates calibrated data files and HTML files. The first processing stage is a flat-fielding. During this step the mean base line is used to find dead pixels (baseline low or at the maximum). Noisy pixels are detected where the standard deviation over the part of the interferogram. A flat-fielded and bad pixel corrected calibration cube using the gain and offset determined by a single blackbody measurement is created. In the second stage each flat-fielded cube is Fourier transformed and a 2-point radiometric calibration is performed. For selected cubes a temperature-emissivity separation algorithm is applied. The second part discusses environmental effects such as diurnal and seasonal atmospheric and temperature changes and the effect of cloud cover on the data. To test the effect of environmental conditions the range-invariant anomaly detection approach is applied to calibrated radiance, brightness temperature and emissivity data.
机译:在本文中,我们描述了使用来自Telops的长波高光谱相机在新泽西州Picatinny Arsenal拍摄的图像傅里叶变换光谱数据的端到端处理。本文的第一部分讨论了从原始数据到校准的辐射度和发射率数据的处理。该数据是在213英尺高的代用坦克目标高塔上,在不同的天气条件下每6分钟在几个月内获取的数据,该项目由美国马里兰州阿德尔菲市的陆军研究实验室赞助。开发了自动校准和分析程序,该程序可创建校准的数据文件和HTML文件。第一个处理阶段是平场。在此步骤中,平均基准线用于查找无效像素(基准线低或最大)。在干涉图部分上的标准偏差处检测到噪点像素。使用单个黑体测量确定的增益和偏移创建一个平整且坏像素校正的校准立方体。在第二阶段中,对每个平场立方体进行傅立叶变换,并执行2点辐射度校准。对于选定的立方体,将应用温度-发射率分离算法。第二部分讨论了环境影响,例如每日和季节性大气和温度变化以及云量对数据的影响。为了测试环境条件的影响,将范围不变的异常检测方法应用于校准后的辐射度,亮度温度和发射率数据。

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