{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T22:51:00Z","timestamp":1769554260705,"version":"3.49.0"},"reference-count":45,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T00:00:00Z","timestamp":1723420800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Demonstration System for High-Resolution Remote Sensing and Mapping Applications (Phase II)","award":["42-Y30B04-9001-19\/21"],"award-info":[{"award-number":["42-Y30B04-9001-19\/21"]}]},{"name":"Demonstration System for High-Resolution Remote Sensing and Mapping Applications (Phase II)","award":["00000113"],"award-info":[{"award-number":["00000113"]}]},{"name":"Construction of natural resources satellite remote sensing technology system and application demonstration","award":["42-Y30B04-9001-19\/21"],"award-info":[{"award-number":["42-Y30B04-9001-19\/21"]}]},{"name":"Construction of natural resources satellite remote sensing technology system and application demonstration","award":["00000113"],"award-info":[{"award-number":["00000113"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Radiation anomalies in optical remote sensing images frequently occur due to electronic issues within the image sensor or data transmission errors. These radiation anomalies can be categorized into several types, including CCD, StripeNoise, RandomCode1, RandomCode2, ImageMissing, and Tap. To ensure the retention of image data with minimal radiation issues as much as possible, this paper adopts a self-made radiation dataset and proposes a FlexVisionNet-YOLO network to detect radiation anomalies more accurately. Firstly, RepViT is used as the backbone network with a vision transformer architecture to better capture global and local features. Its multiscale feature fusion mechanism efficiently handles targets of different sizes and shapes, enhancing the detection ability for radiation anomalies. Secondly, a feature depth fusion network is proposed in the Feature Fusion part, which significantly improves the flexibility and accuracy of feature fusion and thus enhances the detection and classification performance of complex remote sensing images. Finally, Inner-CIoU is used in the Head part for edge regression, which significantly improves the localization accuracy by finely adjusting the target edges; Slide-Loss is used for classification loss, which enhances the classification robustness by dynamically adjusting the category probabilities and markedly improves the classification accuracy, especially in the sample imbalance dataset. Experimental results show that, compared to YOLOv8, the proposed FlexVisionNet-YOLO method improves precision, recall, mAP0.5, and mAP0.5:0.9 by 3.5%, 7.1%, 4.4%, and 13.6%, respectively. Its effectiveness in detecting radiation anomalies surpasses that of other models.<\/jats:p>","DOI":"10.3390\/rs16162953","type":"journal-article","created":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T10:34:59Z","timestamp":1723458899000},"page":"2953","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Radiation Anomaly Detection of Sub-Band Optical Remote Sensing Images Based on Multiscale Deep Dynamic Fusion and Adaptive Optimization"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-1322-2898","authenticated-orcid":false,"given":"Jinlong","family":"Ci","sequence":"first","affiliation":[{"name":"Land Satellite Remote Sensing Application Center, Ministry of Natural Resources of P.R. China, Beijing 100048, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1760-202X","authenticated-orcid":false,"given":"Hai","family":"Tan","sequence":"additional","affiliation":[{"name":"Land Satellite Remote Sensing Application Center, Ministry of Natural Resources of P.R. China, Beijing 100048, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7452-0322","authenticated-orcid":false,"given":"Haoran","family":"Zhai","sequence":"additional","affiliation":[{"name":"Land Satellite Remote Sensing Application Center, Ministry of Natural Resources of P.R. China, Beijing 100048, China"}]},{"given":"Xinming","family":"Tang","sequence":"additional","affiliation":[{"name":"Land Satellite Remote Sensing Application Center, Ministry of Natural Resources of P.R. China, Beijing 100048, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.isprsjprs.2019.04.013","article-title":"Determining switching threshold for NIR-SWIR combined atmospheric correction algorithm of ocean color remote sensing","volume":"153","author":"Liu","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"XiaGS, B. (2018, January 18\u201323). DOTA: ALarge-scale dataset for object detection in aerial images. 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