MSCANet:用于遥感物体探测的多尺度情境感知网络

IF 2.7 4区 地球科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Huaping Zhou, Weidong Liu, Kelei Sun, Jin Wu, Tao Wu
{"title":"MSCANet:用于遥感物体探测的多尺度情境感知网络","authors":"Huaping Zhou, Weidong Liu, Kelei Sun, Jin Wu, Tao Wu","doi":"10.1007/s12145-024-01447-8","DOIUrl":null,"url":null,"abstract":"<p>With the rapid development of remote sensing technology and the widespread application of remote sensing images, remote sensing object detection has become a hot research direction. However, we observe three primary challenges in remote sensing object detection: scale variations, small objects, and complex backgrounds. To address these challenges, we propose a novel detector, he Multi-Scale Context-Aware Network (MSCANet). First, we introduce a Multi-Scale Fusion Module (MSFM) that provides various scales of receptive fields to extract contextual information of objects at different scales adequately. Second, the Multi-Scale Guidance Module (MSGM) is proposed, which fuses deep and shallow feature maps from multiple scales, reducing the loss of feature information in small objects. Finally, we introduce the Context-Aware DownSampling Module (CADM). It dynamically adjusts context information weights at different scales, effectively reducing interference from complex backgrounds. Experimental results demonstrate that the proposed MSCANet achieves superior performance results with mean average precision (mAP) of 97.1% and 73.4% on the challenging RSOD and DIOR datasets, respectively, which indicates that the proposed network is suitable for remote sensing object detection and is of a great reference value.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"74 1","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"MSCANet: A multi-scale context-aware network for remote sensing object detection\",\"authors\":\"Huaping Zhou, Weidong Liu, Kelei Sun, Jin Wu, Tao Wu\",\"doi\":\"10.1007/s12145-024-01447-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>With the rapid development of remote sensing technology and the widespread application of remote sensing images, remote sensing object detection has become a hot research direction. However, we observe three primary challenges in remote sensing object detection: scale variations, small objects, and complex backgrounds. To address these challenges, we propose a novel detector, he Multi-Scale Context-Aware Network (MSCANet). First, we introduce a Multi-Scale Fusion Module (MSFM) that provides various scales of receptive fields to extract contextual information of objects at different scales adequately. Second, the Multi-Scale Guidance Module (MSGM) is proposed, which fuses deep and shallow feature maps from multiple scales, reducing the loss of feature information in small objects. Finally, we introduce the Context-Aware DownSampling Module (CADM). It dynamically adjusts context information weights at different scales, effectively reducing interference from complex backgrounds. Experimental results demonstrate that the proposed MSCANet achieves superior performance results with mean average precision (mAP) of 97.1% and 73.4% on the challenging RSOD and DIOR datasets, respectively, which indicates that the proposed network is suitable for remote sensing object detection and is of a great reference value.</p>\",\"PeriodicalId\":49318,\"journal\":{\"name\":\"Earth Science Informatics\",\"volume\":\"74 1\",\"pages\":\"\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2024-08-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Earth Science Informatics\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1007/s12145-024-01447-8\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earth Science Informatics","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s12145-024-01447-8","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

摘要

随着遥感技术的飞速发展和遥感图像的广泛应用,遥感物体检测已成为一个热门研究方向。然而,我们发现遥感物体检测面临三个主要挑战:尺度变化、小物体和复杂背景。针对这些挑战,我们提出了一种新型检测器--多尺度情境感知网络(MSCANet)。首先,我们引入了多尺度融合模块(MSFM),该模块提供各种尺度的感受野,以充分提取不同尺度物体的上下文信息。其次,我们提出了多尺度引导模块(MSGM),它融合了多个尺度的深层和浅层特征图,减少了小物体特征信息的损失。最后,我们引入了上下文感知下采样模块(CADM)。它能动态调整不同尺度的上下文信息权重,有效减少复杂背景的干扰。实验结果表明,所提出的 MSCANet 在具有挑战性的 RSOD 和 DIOR 数据集上取得了优异的性能结果,平均精度(mAP)分别达到 97.1% 和 73.4%,这表明所提出的网络适用于遥感物体检测,具有很高的参考价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

MSCANet: A multi-scale context-aware network for remote sensing object detection

MSCANet: A multi-scale context-aware network for remote sensing object detection

With the rapid development of remote sensing technology and the widespread application of remote sensing images, remote sensing object detection has become a hot research direction. However, we observe three primary challenges in remote sensing object detection: scale variations, small objects, and complex backgrounds. To address these challenges, we propose a novel detector, he Multi-Scale Context-Aware Network (MSCANet). First, we introduce a Multi-Scale Fusion Module (MSFM) that provides various scales of receptive fields to extract contextual information of objects at different scales adequately. Second, the Multi-Scale Guidance Module (MSGM) is proposed, which fuses deep and shallow feature maps from multiple scales, reducing the loss of feature information in small objects. Finally, we introduce the Context-Aware DownSampling Module (CADM). It dynamically adjusts context information weights at different scales, effectively reducing interference from complex backgrounds. Experimental results demonstrate that the proposed MSCANet achieves superior performance results with mean average precision (mAP) of 97.1% and 73.4% on the challenging RSOD and DIOR datasets, respectively, which indicates that the proposed network is suitable for remote sensing object detection and is of a great reference value.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Earth Science Informatics
Earth Science Informatics COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
4.60
自引率
3.60%
发文量
157
审稿时长
4.3 months
期刊介绍: The Earth Science Informatics [ESIN] journal aims at rapid publication of high-quality, current, cutting-edge, and provocative scientific work in the area of Earth Science Informatics as it relates to Earth systems science and space science. This includes articles on the application of formal and computational methods, computational Earth science, spatial and temporal analyses, and all aspects of computer applications to the acquisition, storage, processing, interchange, and visualization of data and information about the materials, properties, processes, features, and phenomena that occur at all scales and locations in the Earth system’s five components (atmosphere, hydrosphere, geosphere, biosphere, cryosphere) and in space (see "About this journal" for more detail). The quarterly journal publishes research, methodology, and software articles, as well as editorials, comments, and book and software reviews. Review articles of relevant findings, topics, and methodologies are also considered.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信