Fast Object Detection and Localization in Ultrawide Swath Rotating Scan Remote Sensing Images
In the field of remote sensing, rotating scan optical satellites are an important innovation, offering the unique advantage of capturing extensive ground coverage areas with high precision. However, unlike traditional remote sensing images, the inherent characteristics of rotating scan remote sensin...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10824709/ |
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| Summary: | In the field of remote sensing, rotating scan optical satellites are an important innovation, offering the unique advantage of capturing extensive ground coverage areas with high precision. However, unlike traditional remote sensing images, the inherent characteristics of rotating scan remote sensing images make fast object detection and localization a more challenging task because of the ultrawide swath results in extremely high image resolution; changes in the rotation angle cause variations in resolution; the large variation in object size; and side-looking imaging introduces greater geometric distortions. A common solution is to divide the large remote sensing image into smaller (uniform) patches, and then, apply object detection to each small patch. In this article, we investigate image cropping strategies to address these challenges. Specifically, we propose an adaptive image cropping method (AICM) for rotating scan remote sensing images. Starting from the resolution changes caused by variations in the rotation angle, we establish a ground sampling distance model that varies with the rotation angle, achieving adaptive image cropping under different ground sampling distances. Verification on ultrawide swath rotating scan remote sensing images shows that, compared with the traditional fixed stride cropping method, AICM improves the average inference speed by 7.39% and increases the mean average precision by 7.54<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>. |
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| ISSN: | 1939-1404 2151-1535 |