{"title":"利用多尺度特征融合进行遥感场景图像分类的动态卷积协方差网络","authors":"Xinyu Wang;Furong Shi;Haixia Xu;Liming Yuan;Xianbin Wen","doi":"10.1109/JSTARS.2024.3456854","DOIUrl":null,"url":null,"abstract":"The rapid increase in spatial resolution of remote sensing scene images (RSIs) has led to a concomitant increase in the complexity of the spatial contextual information contained therein. The coexistence of numerous smaller features makes it challenging to accurately locate and mine these features, which in turn makes accurate interpretation difficult. In order to address the aforementioned issues, this article proposes a dynamic convolution covariance network (ODFMN) based on omni-dimensional dynamic convolution, which can extract multidimensional and multiscale features from RSIs and perform statistical higher-order representation of feature information. First, in order to fully exploit the complex spatial context information of RSIs and at the same time improve the limitation of a single static convolution kernel for feature extraction, we constructed a omni-dimensional feature extraction module based on dynamic convolution, which fully extracts the 4-D information within the convolution kernel. Then, to make full use of the full-dimensional feature information extracted from each level in the network, the feature representation is enriched by constructing multiscale feature fusion module to establish relationships from local to global. Finally, higher order statistical information is employed to address the challenge of representing first-order information for smaller object features, which is inherently difficult to do. Experiments conducted on publicly available datasets have demonstrated that the method achieves high classification accuracies of 99.04%, 95.34%, and 92.50%, respectively. Furthermore, the method has been verified to have high capture accuracy for feature target contours, shapes, and spatial context information through feature visualization.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10670278","citationCount":"0","resultStr":"{\"title\":\"Dynamic Convolution Covariance Network Using Multiscale Feature Fusion for Remote Sensing Scene Image Classification\",\"authors\":\"Xinyu Wang;Furong Shi;Haixia Xu;Liming Yuan;Xianbin Wen\",\"doi\":\"10.1109/JSTARS.2024.3456854\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The rapid increase in spatial resolution of remote sensing scene images (RSIs) has led to a concomitant increase in the complexity of the spatial contextual information contained therein. The coexistence of numerous smaller features makes it challenging to accurately locate and mine these features, which in turn makes accurate interpretation difficult. In order to address the aforementioned issues, this article proposes a dynamic convolution covariance network (ODFMN) based on omni-dimensional dynamic convolution, which can extract multidimensional and multiscale features from RSIs and perform statistical higher-order representation of feature information. First, in order to fully exploit the complex spatial context information of RSIs and at the same time improve the limitation of a single static convolution kernel for feature extraction, we constructed a omni-dimensional feature extraction module based on dynamic convolution, which fully extracts the 4-D information within the convolution kernel. Then, to make full use of the full-dimensional feature information extracted from each level in the network, the feature representation is enriched by constructing multiscale feature fusion module to establish relationships from local to global. Finally, higher order statistical information is employed to address the challenge of representing first-order information for smaller object features, which is inherently difficult to do. Experiments conducted on publicly available datasets have demonstrated that the method achieves high classification accuracies of 99.04%, 95.34%, and 92.50%, respectively. Furthermore, the method has been verified to have high capture accuracy for feature target contours, shapes, and spatial context information through feature visualization.\",\"PeriodicalId\":13116,\"journal\":{\"name\":\"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2024-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10670278\",\"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/10670278/\",\"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/10670278/","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}
Dynamic Convolution Covariance Network Using Multiscale Feature Fusion for Remote Sensing Scene Image Classification
The rapid increase in spatial resolution of remote sensing scene images (RSIs) has led to a concomitant increase in the complexity of the spatial contextual information contained therein. The coexistence of numerous smaller features makes it challenging to accurately locate and mine these features, which in turn makes accurate interpretation difficult. In order to address the aforementioned issues, this article proposes a dynamic convolution covariance network (ODFMN) based on omni-dimensional dynamic convolution, which can extract multidimensional and multiscale features from RSIs and perform statistical higher-order representation of feature information. First, in order to fully exploit the complex spatial context information of RSIs and at the same time improve the limitation of a single static convolution kernel for feature extraction, we constructed a omni-dimensional feature extraction module based on dynamic convolution, which fully extracts the 4-D information within the convolution kernel. Then, to make full use of the full-dimensional feature information extracted from each level in the network, the feature representation is enriched by constructing multiscale feature fusion module to establish relationships from local to global. Finally, higher order statistical information is employed to address the challenge of representing first-order information for smaller object features, which is inherently difficult to do. Experiments conducted on publicly available datasets have demonstrated that the method achieves high classification accuracies of 99.04%, 95.34%, and 92.50%, respectively. Furthermore, the method has been verified to have high capture accuracy for feature target contours, shapes, and spatial context information through feature visualization.
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.