A Robust Oriented Filter-Based Matching Method for Multisource, Multitemporal Remote Sensing Images

Fan, Zhongli; Wang, Mi; Pi, Yifei; Liu, Yuxuan; Jiang, Huiwei

doi:10.1109/tgrs.2023.3288531

Cited by 6 publications

(1 citation statement)

References 40 publications

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“…Table 2 displays the quantitative results of the proposed method and two state-of-theart comparison methods, including the Robust Oriented Filter-Based Matching Method (OFM) [32] and OSMNet [33]. All methods were implemented on a personal computer equipped with an Intel Core i9 12th generation CPU and an RTX 3060 GPU.…”

Section: Results Of On-ground Experiments For Micro-sar Satellitementioning

confidence: 99%

On-Board Geometric Rectification for Micro-Satellite Based on Lightweight Feature Database

Wang,

Xiang,

Wang

et al. 2023

Remote Sensing

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On-board processing is increasingly prevalent due to its efficient utilization of satellite resources. Among these resources, geometric rectification can significantly enhance positioning accuracy for subsequent tasks, such as object detection. This approach mitigates the heavy burden on downlink bandwidth and minimizes time delays by transmitting targeted patches rather than raw data. However, existing rectification methods are often unsuitable due to the limitations and conditions imposed on satellites. Factors like hardware quality, heat dissipation, storage space, and geographic positioning are frequently constrained and prone to inaccuracies. This paper proposes a novel on-board rectification method. The method introduces a two-step matching framework to address substantial positioning errors and incorporates a feature-compression strategy to reduce the storage space of reference patches. Quantitative and practical experiments demonstrate the method’s efficacy in terms of storage space, time efficiency, and geometric rectification accuracy.

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Section: Results Of On-ground Experiments For Micro-sar Satellitementioning

confidence: 99%