当前位置: X-MOL 学术Remote Sens. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
A Fast Iterative Procedure for Adjacency Effects Correction on Remote Sensed Data
Remote Sensing ( IF 4.2 ) Pub Date : 2021-05-05 , DOI: 10.3390/rs13091799
Donatella Guzzi , Vanni Nardino , Cinzia Lastri , Valentina Raimondi

This paper describes a simple, iterative atmospheric correction procedure based on the MODTRAN®5 radiative transfer code. Such a procedure receives in input a spectrally resolved at-sensor radiance image, evaluates the different contributions to received radiation, and corrects the effect of adjacency from surrounding pixels permitting the retrieval of ground reflectance spectrum for each pixel of the image. The procedure output is a spectral ground reflectance image obtained without the need of any user-provided a priori hypothesis. The novelty of the proposed method relies on its iterative approach for evaluating the contribution of surrounding pixels: a first run of the atmospheric correction procedure is performed by assuming that the spectral reflectance of the surrounding pixels is equal to that of the pixel under investigation. Such information is used in the subsequent iteration steps to estimate the spectral radiance of the surrounding pixels, in order to make a more accurate evaluation of the reflectance image. The results are here presented and discussed for two different cases: synthetic images produced with the hyperspectral simulation tool PRIMUS and real images acquired by CHRIS–PROBA sensor. The retrieved reflectance error drops after a few iterations, providing a quantitative estimate for the number of iterations needed. Relative error after the procedure converges is in the order of few percent, and the causes of remaining uncertainty in retrieved spectra are discussed.

中文翻译:

遥感数据邻接效应校正的快速迭代过程

本文描述了一种基于MODTRAN一个简单的,迭代大气校正程序®5辐射转移码。这样的过程在输入中接收光谱解析的传感器辐射度图像,评估对接收辐射的不同贡献,并校正来自周围像素的邻接的影响,从而允许检索图像的每个像素的地面反射光谱。该过程输出是不需要任何用户提供的先验假设而获得的光谱地面反射率图像。所提出的方法的新颖性取决于其用于评估周围像素贡献的迭代方法:通过假设周围像素的光谱反射率等于所研究像素的光谱反射率来执行大气校正程序的第一轮。此类信息在后续的迭代步骤中用于估计周围像素的光谱辐射度,以便对反射率图像进行更准确的评估。本文介绍和讨论了两种不同情况的结果:使用高光谱仿真工具PRIMUS生成的合成图像和通过CHRIS-PROBA传感器获取的真实图像。几次迭代后,检索到的反射率误差下降,从而为所需的迭代次数提供了定量估计。该过程收敛后的相对误差约为百分之几,并讨论了所获取光谱中仍存在不确定性的原因。使用高光谱模拟工具PRIMUS生成的合成图像和通过CHRIS–PROBA传感器获取的真实图像。几次迭代后,检索到的反射率误差下降,从而为所需的迭代次数提供了定量估计。该过程收敛后的相对误差约为百分之几,并讨论了所获取光谱中仍存在不确定性的原因。使用高光谱模拟工具PRIMUS生成的合成图像和通过CHRIS–PROBA传感器获取的真实图像。几次迭代后,检索到的反射率误差下降,从而为所需的迭代次数提供了定量估计。该过程收敛后的相对误差约为百分之几,并讨论了所获取光谱中仍存在不确定性的原因。
更新日期:2021-05-06
down
wechat
bug