Jun Xie;Tingting Tian;Richa Hu;Xuan Yang;Yue Xu;Luyang Zan
{"title":"大范围、多场景遥感图像中风力涡轮机的新型探测器","authors":"Jun Xie;Tingting Tian;Richa Hu;Xuan Yang;Yue Xu;Luyang Zan","doi":"10.1109/JSTARS.2024.3460730","DOIUrl":null,"url":null,"abstract":"Wind turbines are one of the important carriers of clean energy utilization. Accurately and rapidly detecting wind turbine objects in large-scale remote sensing images can effectively monitor the development activities and optimize energy utilization. Addressing the detection challenges posed by the complex distribution scenes and the slender, dispersed structural characteristics of wind turbines in remote sensing images, this article proposes a remote sensing image wind turbine detector, RSWDet, based on neural networks. RSWDet comprises two innovative key modules. The first is a dual-branch structured point set detection head, which, through training, adapts to the unique features of wind turbines, enabling accurate detection in large-scale complex backgrounds. The second is the Low-level Feature Enhancement module, which compensates for the loss of wind turbine feature information during sampling by leveraging rich low-level feature information. Experimental verification of RSWDet was conducted on datasets and real-world scenes. The results demonstrate that RSWDet exhibits significant advantages compared to other algorithms, achieving the highest average accuracy of 83.1%, Precision of 97.8%, and Recall of 99% on the validation set. In the actual multiscene GF2 remote sensing image test, with a threshold of 0.4, the Precision can reach 85.3%, and the Recall can reach 89.9%.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10680199","citationCount":"0","resultStr":"{\"title\":\"A Novel Detector for Wind Turbines in Wide-Ranging, Multiscene Remote Sensing Images\",\"authors\":\"Jun Xie;Tingting Tian;Richa Hu;Xuan Yang;Yue Xu;Luyang Zan\",\"doi\":\"10.1109/JSTARS.2024.3460730\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Wind turbines are one of the important carriers of clean energy utilization. Accurately and rapidly detecting wind turbine objects in large-scale remote sensing images can effectively monitor the development activities and optimize energy utilization. Addressing the detection challenges posed by the complex distribution scenes and the slender, dispersed structural characteristics of wind turbines in remote sensing images, this article proposes a remote sensing image wind turbine detector, RSWDet, based on neural networks. RSWDet comprises two innovative key modules. The first is a dual-branch structured point set detection head, which, through training, adapts to the unique features of wind turbines, enabling accurate detection in large-scale complex backgrounds. The second is the Low-level Feature Enhancement module, which compensates for the loss of wind turbine feature information during sampling by leveraging rich low-level feature information. Experimental verification of RSWDet was conducted on datasets and real-world scenes. The results demonstrate that RSWDet exhibits significant advantages compared to other algorithms, achieving the highest average accuracy of 83.1%, Precision of 97.8%, and Recall of 99% on the validation set. In the actual multiscene GF2 remote sensing image test, with a threshold of 0.4, the Precision can reach 85.3%, and the Recall can reach 89.9%.\",\"PeriodicalId\":13116,\"journal\":{\"name\":\"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2024-09-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10680199\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10680199/\",\"RegionNum\":2,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10680199/","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
A Novel Detector for Wind Turbines in Wide-Ranging, Multiscene Remote Sensing Images
Wind turbines are one of the important carriers of clean energy utilization. Accurately and rapidly detecting wind turbine objects in large-scale remote sensing images can effectively monitor the development activities and optimize energy utilization. Addressing the detection challenges posed by the complex distribution scenes and the slender, dispersed structural characteristics of wind turbines in remote sensing images, this article proposes a remote sensing image wind turbine detector, RSWDet, based on neural networks. RSWDet comprises two innovative key modules. The first is a dual-branch structured point set detection head, which, through training, adapts to the unique features of wind turbines, enabling accurate detection in large-scale complex backgrounds. The second is the Low-level Feature Enhancement module, which compensates for the loss of wind turbine feature information during sampling by leveraging rich low-level feature information. Experimental verification of RSWDet was conducted on datasets and real-world scenes. The results demonstrate that RSWDet exhibits significant advantages compared to other algorithms, achieving the highest average accuracy of 83.1%, Precision of 97.8%, and Recall of 99% on the validation set. In the actual multiscene GF2 remote sensing image test, with a threshold of 0.4, the Precision can reach 85.3%, and the Recall can reach 89.9%.
期刊介绍:
The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.