WEKO3
アイテム
Onboard Data Prioritization Using Multi-Class Image Segmentation for Nanosatellites
http://hdl.handle.net/10228/0002002003
http://hdl.handle.net/10228/00020020033864c13b-36fe-413a-8032-b05df93b0a75
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
10464181.pdf (2.7 MB)
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| Item type | 共通アイテムタイプ(1) | |||||||||||||
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| 公開日 | 2025-09-19 | |||||||||||||
| タイトル | ||||||||||||||
| タイトル | Onboard Data Prioritization Using Multi-Class Image Segmentation for Nanosatellites | |||||||||||||
| 言語 | en | |||||||||||||
| 著者 |
Chatar, Keenan
× Chatar, Keenan
× Kitamura, Kentaro
× 趙, 孟佑
WEKO
754
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| 著作権関連情報 | ||||||||||||||
| 言語 | en | |||||||||||||
| 権利情報Resource | https://creativecommons.org/licenses/by/4.0/ | |||||||||||||
| 権利情報 | Copyright (c) 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | |||||||||||||
| 抄録 | ||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||
| 内容記述 | Nanosatellites are proliferating as low-cost, dedicated remote sensing opportunities for small nations. However, nanosatellites’ performance as remote sensing platforms is impaired by low downlink speeds, which typically range from 1200 to 9600 bps. Additionally, an estimated 67% of downloaded data are unusable for further applications due to excess cloud cover. To alleviate this issue, we propose an image segmentation and prioritization algorithm to classify and segment the contents of captured images onboard the nanosatellite. This algorithm prioritizes images with clear captures of water bodies and vegetated areas with high downlink priority. This in-orbit organization of images will aid ground station operators with downlinking images suitable for further ground-based remote sensing analysis. The proposed algorithm uses Convolutional Neural Network (CNN) models to classify and segment captured image data. In this study, we compare various model architectures and backbone designs for segmentation and assess their performance. The models are trained on a dataset that simulates captured data from nanosatellites and transferred to the satellite hardware to conduct inferences. Ground testing for the satellite has achieved a peak Mean IoU of 75% and an F1 Score of 0.85 for multi-class segmentation. The proposed algorithm is expected to improve data budget downlink efficiency by up to 42% based on validation testing. | |||||||||||||
| 言語 | en | |||||||||||||
| 書誌情報 |
en : Remote Sensing 巻 16, 号 10, p. 1729, 発行日 2024-05-13 |
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| 出版社 | ||||||||||||||
| 出版者 | MDPI | |||||||||||||
| 言語 | en | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | image segmentation | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | classification | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | CubeSat | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | Nanosatellite | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | onboard processing | |||||||||||||
| 言語 | ||||||||||||||
| 言語 | eng | |||||||||||||
| 資源タイプ | ||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||
| 資源タイプ | journal article | |||||||||||||
| 出版タイプ | ||||||||||||||
| 出版タイプ | VoR | |||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||
| DOI | ||||||||||||||
| 識別子タイプ | DOI | |||||||||||||
| 関連識別子 | https://doi.org/10.3390/rs16101729 | |||||||||||||
| ISSN | ||||||||||||||
| 収録物識別子タイプ | EISSN | |||||||||||||
| 収録物識別子 | 2072-4292 | |||||||||||||
| 研究者情報 | ||||||||||||||
| URL | https://hyokadb02.jimu.kyutech.ac.jp/html/168_ja.html | |||||||||||||
| 論文ID（連携） | ||||||||||||||
| 値 | 10464181 | |||||||||||||
| 連携ID | ||||||||||||||
| 値 | 15278 | |||||||||||||
