gift SM EDU数据处理及算法

J. Tian, David G. Johnson, R. Reisse, M. Gazarik
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引用次数: 1

摘要

地球同步成像傅里叶变换光谱仪(GIFTS)传感器模块(SM)工程演示单元(EDU)是一种高分辨率光谱成像仪,设计用于使用傅里叶变换光谱仪(FTS)测量红外(IR)辐射。gift仪器采用三个焦平面阵列(fpa),收集长波红外(LWIR)、短波/中波红外(SMWIR)和可见光谱波段的测量数据。对原始干涉图测量进行辐射和光谱校准以产生辐射光谱,并通过检索算法对其进行进一步处理以获得大气剖面。本文介绍了标定阶段所涉及的处理算法。校正程序可细分为三个阶段。在预校正阶段,对抽取滤波后的复杂干涉图进行相位校正。得到的频谱虚部只包含未校正频谱的噪声分量。通过对相位校正后的黑体参考光谱应用谱平滑程序,可以实现额外的随机噪声抑制。在辐射定标阶段,我们首先根据之前的结果计算光谱响应度,由此得到校准后的环境黑体(ABB)、热黑体(HBB)和场景光谱。在后处理阶段,我们从校准的ABB和HBB光谱估计噪声等效光谱辐射(NESR)。然后,我们实现了一种补偿前光学偏移影响的校正方案。最后,对离轴像素进行FPA离轴效果校正。为了评估整个FPA的性能,我们开发了一种有效的生成像素性能评估的方法。在此基础上,设计了基于像素性能评价的随机像素选择方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
GIFTS SM EDU data processing and algorithms
The geosynchronous imaging Fourier transform spectrometer (GIFTS) sensor module (SM) engineering demonstration unit (EDU) is a high resolution spectral imager designed to measure infrared (IR) radiances using a Fourier transform spectrometer (FTS). The GIFTS instrument employs three focal plane arrays (FPAs), which gather measurements across the long-wave IR (LWIR), short/mid-wave IR (SMWIR), and visible spectral bands. The raw interferogram measurements are radiometrically and spectrally calibrated to produce radiance spectra, which are further processed to obtain atmospheric profiles via retrieval algorithms. This paper describes the processing algorithms involved in the calibration stage. The calibration procedures can be subdivided into three stages. In the pre-calibration stage, a phase correction algorithm is applied to the decimated and filtered complex interferogram. The resulting imaginary part of the spectrum contains only the noise component of the uncorrected spectrum. Additional random noise reduction can be accomplished by applying a spectral smoothing routine to the phase-corrected blackbody reference spectra. In the radiometric calibration stage, we first compute the spectral responsivity based on the previous results, from which, the calibrated ambient blackbody (ABB), hot blackbody (HBB), and scene spectra can be obtained. During the post-processing stage, we estimate the noise equivalent spectral radiance (NESR) from the calibrated ABB and HBB spectra. We then implement a correction scheme that compensates for the effect of fore-optics offsets. Finally, for off-axis pixels, the FPA off-axis effects correction is performed. To estimate the performance of the entire FPA, we developed an efficient method of generating pixel performance assessments. In addition, a random pixel selection scheme is designed based on the pixel performance evaluation.
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