Accurate fine-grained geospatial scene classification using remote sensing imagery is essential for a wide range of applications.However,existing approaches often rely on manually zooming remote sensing images at diff...Accurate fine-grained geospatial scene classification using remote sensing imagery is essential for a wide range of applications.However,existing approaches often rely on manually zooming remote sensing images at different scales to create typical scene samples.This approach fails to adequately support the fixed-resolution image interpretation requirements in real-world scenarios.To address this limitation,we introduce the million-scale fine-grained geospatial scene classification dataset(MEET),which contains over 1.03 million zoom-free remote sensing scene samples,manually annotated into 80 fine-grained categories.In MEET,each scene sample follows a scene-in-scene layout,where the central scene serves as the reference,and auxiliary scenes provide crucial spatial context for fine-grained classification.Moreover,to tackle the emerging challenge of scene-in-scene classification,we present the context-aware transformer(CAT),a model specifically designed for this task,which adaptively fuses spatial context to accurately classify the scene samples.CAT adaptively fuses spatial context to accurately classify the scene samples by learning attentional features that capture the relationships between the center and auxiliary scenes.Based on MEET,we establish a comprehensive benchmark for fine-grained geospatial scene classification,evaluating CAT against 11 competitive baselines.The results demonstrate that CAT significantly outperforms these baselines,achieving a 1.88%higher balanced accuracy(BA)with the Swin-Large backbone,and a notable 7.87%improvement with the Swin-Huge backbone.Further experiments validate the effectiveness of each module in CAT and show the practical applicability of CAT in the urban functional zone mapping.The source code and dataset will be publicly available at https://jerrywyn.github.io/project/MEET.html.展开更多
With the application of the articulated phase insulator, and the speed of electric locomotive rising, it is inevitable for the electric locomotive to adopt the technology automatic passing through the electric phase s...With the application of the articulated phase insulator, and the speed of electric locomotive rising, it is inevitable for the electric locomotive to adopt the technology automatic passing through the electric phase separation. However, when the locomotive passes the electric phase separation, a variety of overvoltages will be generated, such as the cut-off overvoltage and the closing overvoltage. In this paper, the causes of the two overvoltages above are analyzed theoretically and simulated in Simulink. Then this paper discusses the suppression effects on the cut-off overvoltage and the closing overvoltage by paralleling the nonlinear resistance and the main breaker, or parallelling the nonlinear resistance and the locomotive transformer. The simulation results show that parallelling the nonlinear resistance and the locomotive transformer has suppressive effects on the two overvoltages mentioned above.展开更多
基金supported by the National Natural Science Foundation of China(42030102,42371321).
文摘Accurate fine-grained geospatial scene classification using remote sensing imagery is essential for a wide range of applications.However,existing approaches often rely on manually zooming remote sensing images at different scales to create typical scene samples.This approach fails to adequately support the fixed-resolution image interpretation requirements in real-world scenarios.To address this limitation,we introduce the million-scale fine-grained geospatial scene classification dataset(MEET),which contains over 1.03 million zoom-free remote sensing scene samples,manually annotated into 80 fine-grained categories.In MEET,each scene sample follows a scene-in-scene layout,where the central scene serves as the reference,and auxiliary scenes provide crucial spatial context for fine-grained classification.Moreover,to tackle the emerging challenge of scene-in-scene classification,we present the context-aware transformer(CAT),a model specifically designed for this task,which adaptively fuses spatial context to accurately classify the scene samples.CAT adaptively fuses spatial context to accurately classify the scene samples by learning attentional features that capture the relationships between the center and auxiliary scenes.Based on MEET,we establish a comprehensive benchmark for fine-grained geospatial scene classification,evaluating CAT against 11 competitive baselines.The results demonstrate that CAT significantly outperforms these baselines,achieving a 1.88%higher balanced accuracy(BA)with the Swin-Large backbone,and a notable 7.87%improvement with the Swin-Huge backbone.Further experiments validate the effectiveness of each module in CAT and show the practical applicability of CAT in the urban functional zone mapping.The source code and dataset will be publicly available at https://jerrywyn.github.io/project/MEET.html.
文摘With the application of the articulated phase insulator, and the speed of electric locomotive rising, it is inevitable for the electric locomotive to adopt the technology automatic passing through the electric phase separation. However, when the locomotive passes the electric phase separation, a variety of overvoltages will be generated, such as the cut-off overvoltage and the closing overvoltage. In this paper, the causes of the two overvoltages above are analyzed theoretically and simulated in Simulink. Then this paper discusses the suppression effects on the cut-off overvoltage and the closing overvoltage by paralleling the nonlinear resistance and the main breaker, or parallelling the nonlinear resistance and the locomotive transformer. The simulation results show that parallelling the nonlinear resistance and the locomotive transformer has suppressive effects on the two overvoltages mentioned above.
