目前,对地观测多平台光学载荷组网协同观测技术日臻成熟,感兴趣区域的高精度高频次跟踪愈捷高效。由于对地观测影像获取时间、观测几何及大气条件等因素各异,组网平台载荷所获取的同一地物多源影像数据之间辐射一致性较差,目视效果悬殊,严重限制了数据的后期保障应用。本文聚焦高分辨可见光影像数据,深入探索多平台时序影像数据的辐射一致性处理框架,创新性提出了融合辐射传输和非参数回归的多平台光学载荷辐射一致性校正方法。该方法通过稳定场大气层顶反射率模型为核心的多要素转换,实现了对地观测平台载荷间辐射再定标和大气层顶辐射差异校正。进一步地,在消除大气效应和二向反射分布函数（Bidirectional reflectance distribution function,BRDF）校正的基础上,充分利用对地观测平台载荷数据重叠区域的大气层底辐射信息,建立数据辐射连接关系,发展了基于局部加权回归（Locally Weighted Scatterplot Smoothing,LOESS）的对地观测平台载荷辐射一致性校正方法,攻克多时相影像辐射量值不一致难题。经高分辨相机多天影像验证结果表明：利用本文提出的辐射一致性校正方法对增强对地观测平台载荷影像辐射一致性效果显著,有力推动了多源遥感影像的协同应用向纵深发展。
赵薇薇
,
王艳
,
曲小飞
,
李婉
,
高彩霞
,
马灵玲
. 融合辐射传输和非参数回归的多平台光学载荷辐射一致性校正方法*[J]. 中国科学院大学学报, 0
: 2025110501
-2025110501
.
DOI: 10.7523/j.ucas. 2025.050
Currently, the networked collaborative observation technology using multi-platform optical payloads for Earth observation (EO) has matured significantly, enabling highly efficient and frequent monitoring of regions of interest. However, substantial radiometric inconsistencies persist among multi-source EO data acquired by networked platform payloads over identical ground target, primarily due to variations in acquisition time, observation geometry, and atmospheric conditions. These discrepancies result in disparate visual effects that severely constrain subsequent data applications. This study focuses on high-resolution visible images, systematically investigates a radiometric consistency processing framework for multi-platform time-series images, and innovatively proposes a radiometric consistency correction method that integrates radioactive transfer theory with nonparametric regression analysis. In this methodology, a top-of-atmosphere (TOA) reflectance model of stable target is used to perform multi-observation parameter conversion, effectively achieving inter-platform radiometric recalibration and TOA radiometric difference correction. Furthermore, after eliminating atmospheric effect and correcting Bidirectional Reflectance Distribution Function (BRDF), we fully exploit the bottom-of-atmosphere (BOA) radiation information from the overlapping areas of the images observed by payloads onboard different EO platforms, so as to establish radiometric connection relationships, and develop a local weighted regression-based (Locally Estimated Scatterplot Smoothing, LOESS) radiometric consistency correction method, successfully addressing the challenge of radiation value inconsistency in multi-temporal images. Validation experiments using multi-temporal high-resolution camera imagery demonstrate that the proposed radiometric consistency correction method significantly enhances radiometric consistency across those images observed by sensors onboard different EO platforms. This advancement substantially promotes the synergistic application of multi-source remote sensing data towards more sophisticated levels.
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