{"title":"重新感知遥感图像弱监督目标定位的变压器全局视图","authors":"Xuran Hu;Mingzhe Zhu;Zhenpeng Feng;Ljubiša Stanković","doi":"10.1109/JSTARS.2024.3459792","DOIUrl":null,"url":null,"abstract":"In recent decades, weakly supervised object localization (WSOL) has gained increasing attention in remote sensing. However, unlike optical images, remote sensing images (RSIs) often contain more complex scenes, which poses challenges for WSOL. Traditional convolutional neural network (CNN)-based WSOL methods are often limited by a small receptive field and yield unsatisfactory results. Transformer-based methods can obtain global perception, addressing the limitations of receptive fields in CNN-based methods, yet it may also introduce attention diffusion. To address the aforementioned problems, this article proposes a novel WSOL method based on an interpretable vision transformer (ViT), RPGV. We introduce a feature fusion enhancement module to obtain the saliency map that captures global information. Simultaneously, we solve the problem of discrete attention in the traditional ViT and eliminate local distortion in the feature map by introducing a global semantic screening module. We conduct comprehensive experiments on DIOR and HRRSD datasets, demonstrating the superior performance of our method compared to current state-of-the-art methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10678922","citationCount":"0","resultStr":"{\"title\":\"Reperceive Global Vision of Transformer for Remote Sensing Images Weakly Supervised Object Localization\",\"authors\":\"Xuran Hu;Mingzhe Zhu;Zhenpeng Feng;Ljubiša Stanković\",\"doi\":\"10.1109/JSTARS.2024.3459792\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recent decades, weakly supervised object localization (WSOL) has gained increasing attention in remote sensing. However, unlike optical images, remote sensing images (RSIs) often contain more complex scenes, which poses challenges for WSOL. Traditional convolutional neural network (CNN)-based WSOL methods are often limited by a small receptive field and yield unsatisfactory results. Transformer-based methods can obtain global perception, addressing the limitations of receptive fields in CNN-based methods, yet it may also introduce attention diffusion. To address the aforementioned problems, this article proposes a novel WSOL method based on an interpretable vision transformer (ViT), RPGV. We introduce a feature fusion enhancement module to obtain the saliency map that captures global information. Simultaneously, we solve the problem of discrete attention in the traditional ViT and eliminate local distortion in the feature map by introducing a global semantic screening module. We conduct comprehensive experiments on DIOR and HRRSD datasets, demonstrating the superior performance of our method compared to current state-of-the-art methods.\",\"PeriodicalId\":13116,\"journal\":{\"name\":\"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2024-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10678922\",\"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/10678922/\",\"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/10678922/","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}
Reperceive Global Vision of Transformer for Remote Sensing Images Weakly Supervised Object Localization
In recent decades, weakly supervised object localization (WSOL) has gained increasing attention in remote sensing. However, unlike optical images, remote sensing images (RSIs) often contain more complex scenes, which poses challenges for WSOL. Traditional convolutional neural network (CNN)-based WSOL methods are often limited by a small receptive field and yield unsatisfactory results. Transformer-based methods can obtain global perception, addressing the limitations of receptive fields in CNN-based methods, yet it may also introduce attention diffusion. To address the aforementioned problems, this article proposes a novel WSOL method based on an interpretable vision transformer (ViT), RPGV. We introduce a feature fusion enhancement module to obtain the saliency map that captures global information. Simultaneously, we solve the problem of discrete attention in the traditional ViT and eliminate local distortion in the feature map by introducing a global semantic screening module. We conduct comprehensive experiments on DIOR and HRRSD datasets, demonstrating the superior performance of our method compared to current state-of-the-art methods.
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.