Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmentalmonitoring.Addressing the limitations of conventional convolutional neural networks,we propose ...Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmentalmonitoring.Addressing the limitations of conventional convolutional neural networks,we propose an innovative transformer-based method.This method leverages transformers,which are adept at processing data sequences,to enhance cloud detection accuracy.Additionally,we introduce a Cyclic Refinement Architecture that improves the resolution and quality of feature extraction,thereby aiding in the retention of critical details often lost during cloud detection.Our extensive experimental validation shows that our approach significantly outperforms established models,excelling in high-resolution feature extraction and precise cloud segmentation.By integrating Positional Visual Transformers(PVT)with this architecture,our method advances high-resolution feature delineation and segmentation accuracy.Ultimately,our research offers a novel perspective for surmounting traditional challenges in cloud detection and contributes to the advancement of precise and dependable image analysis across various domains.展开更多
The advantages and disadvantages of two existing methods for explosive field visualization are analyzed in this paper. And a new method based on image fusion is proposed to integrate their complementary advantages. Wi...The advantages and disadvantages of two existing methods for explosive field visualization are analyzed in this paper. And a new method based on image fusion is proposed to integrate their complementary advantages. With the method, two source images built by equal mapping and modulus mapping are individually decomposed into two Gauss-Laplacian pyramid sequences. Then, the two individual sequences are used to make a composite one according to the process of fusion. Finally, a new image is reconstructed from the composite sequence. Experimental results show that the new images integrate the advantages of sources, effectively improve the visualization, and disclose more information about explosive field.展开更多
基金funded by the Chongqing Normal University Startup Foundation for PhD(22XLB021)supported by the Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China(No.ICT2023B40).
文摘Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmentalmonitoring.Addressing the limitations of conventional convolutional neural networks,we propose an innovative transformer-based method.This method leverages transformers,which are adept at processing data sequences,to enhance cloud detection accuracy.Additionally,we introduce a Cyclic Refinement Architecture that improves the resolution and quality of feature extraction,thereby aiding in the retention of critical details often lost during cloud detection.Our extensive experimental validation shows that our approach significantly outperforms established models,excelling in high-resolution feature extraction and precise cloud segmentation.By integrating Positional Visual Transformers(PVT)with this architecture,our method advances high-resolution feature delineation and segmentation accuracy.Ultimately,our research offers a novel perspective for surmounting traditional challenges in cloud detection and contributes to the advancement of precise and dependable image analysis across various domains.
基金Sponsored by the National Natural Science Foundation of China(10625208)the Basic Research Foundation of Beijing Institute of Technology(20061242005)the Foundation of State Key Laboratory of Explosion Science and Technology(ZDKT08-02)
文摘The advantages and disadvantages of two existing methods for explosive field visualization are analyzed in this paper. And a new method based on image fusion is proposed to integrate their complementary advantages. With the method, two source images built by equal mapping and modulus mapping are individually decomposed into two Gauss-Laplacian pyramid sequences. Then, the two individual sequences are used to make a composite one according to the process of fusion. Finally, a new image is reconstructed from the composite sequence. Experimental results show that the new images integrate the advantages of sources, effectively improve the visualization, and disclose more information about explosive field.
