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GOES-R series image navigation and registration performance assessment tool set
Journal of Applied Remote Sensing ( IF 1.4 ) Pub Date : 2020-04-08 , DOI: 10.1117/1.jrs.14.032405
Bin Tan 1 , John J. Dellomo 1 , Christopher N. Folley 2 , Thomas J. Grycewicz 2 , Scott Houchin 2 , Peter J. Isaacson 2 , Patrick D. Johnson 2 , Brian C. Porter 2 , Alan D. Reth 1 , Pradeep Thiyanaratnam 2 , Robert E. Wolfe 1
Affiliation  

Abstract. An image navigation (NAV) and registration (INR) performance assessment tool set (IPATS) was developed to assess the US Geostationary Operational Environmental Satellite R-series (GOES-R) Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) INR performance. IPATS produces five INR metrics for level 1B ABI images: navigation, channel-to-channel registration, frame-to-frame registration, swath-to-swath registration, and within-frame registration. IPATS also produces one INR metric for GLM: navigation of background images. The high-precision INR metrics produced by IPATS are critical to INR performance evaluation and long-term monitoring. IPATS INR metrics also provide feedback to INR engineers for tuning the navigation algorithms and parameters to further refine INR performance. IPATS utilizes a modular algorithm design to allow the user-selectable data processing sequence and configuration parameters. We first describe the algorithmic design and the implementation of IPATS. Next, it describes the investigation of the optimization of the configuration parameters to reduce measurement errors. Finally, sample INR performance is presented, including GOES-16 and GOES-17 ABI NAV performance from postlaunch test to November 2019 and the comparison of example 24-h INR performance against the mission performance requirements. The INR assessment results show that both GOES-R ABIs are in compliance with the mission INR requirements.

中文翻译:

GOES-R系列图像导航配准性能评估工具集

摘要。开发了图像导航 (NAV) 和配准 (INR) 性能评估工具集 (IPATS) 来评估美国地球同步运行环境卫星 R 系列 (GOES-R) 高级基线成像仪 (ABI) 和地球同步闪电测绘仪 (GLM) INR表现。IPATS 为 1B 级 ABI 图像生成五个 INR 指标:导航、通道到通道配准、帧到帧配准、条带到条带配准和帧内配准。IPATS 还为 GLM 生成一个 INR 指标:背景图像导航。IPATS 产生的高精度 INR 指标对于 INR 性能评估和长期监测至关重要。IPATS INR 指标还向 INR 工程师提供反馈,以调整导航算法和参数以进一步改进 INR 性能。IPATS 采用模块化算法设计,允许用户选择数据处理顺序和配置参数。我们首先描述了 IPATS 的算法设计和实现。接下来,它描述了优化配置参数以减少测量误差的调查。最后,展示了样本 INR 性能,包括从发射后测试到 2019 年 11 月的 GOES-16 和 GOES-17 ABI NAV 性能,以及示例 24 小时 INR 性能与任务性能要求的比较。INR评估结果表明,两个GOES-R ABI均符合任务INR要求。它描述了优化配置参数以减少测量误差的调查。最后,展示了样本 INR 性能,包括从发射后测试到 2019 年 11 月的 GOES-16 和 GOES-17 ABI NAV 性能,以及示例 24 小时 INR 性能与任务性能要求的比较。INR评估结果表明,两个GOES-R ABI均符合任务INR要求。它描述了对优化配置参数以减少测量误差的调查。最后,展示了样本 INR 性能,包括从发射后测试到 2019 年 11 月的 GOES-16 和 GOES-17 ABI NAV 性能,以及示例 24 小时 INR 性能与任务性能要求的比较。INR评估结果表明,两个GOES-R ABI均符合任务INR要求。
更新日期:2020-04-08
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