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
针对反舰武器图像制导目标实例分割精度低,模型中上下文语义交互不充分,特征融合推理速度慢,数据集难易样本不均衡导致训练效果差等问题,提出了一种基于改进滑动窗口的Transformer(shifted windows Transformer,Swin Transformer)的舰...针对反舰武器图像制导目标实例分割精度低,模型中上下文语义交互不充分,特征融合推理速度慢,数据集难易样本不均衡导致训练效果差等问题,提出了一种基于改进滑动窗口的Transformer(shifted windows Transformer,Swin Transformer)的舰船目标实例分割算法。设计了局部增强感知模块用以拓展感受野,加强语义交互能力;采用反向特征金字塔网络进行特征融合,提高算法处理速度;使用在线困难样例挖掘,改善数据集样本不均衡问题,提升网络训练效果。实验结果表明,改进后的算法相较基线算法在分割准确率上提升了1.5%,在处理速度上提高了1.3%,兼具精度和速度优势。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.
文摘针对反舰武器图像制导目标实例分割精度低,模型中上下文语义交互不充分,特征融合推理速度慢,数据集难易样本不均衡导致训练效果差等问题,提出了一种基于改进滑动窗口的Transformer(shifted windows Transformer,Swin Transformer)的舰船目标实例分割算法。设计了局部增强感知模块用以拓展感受野,加强语义交互能力;采用反向特征金字塔网络进行特征融合,提高算法处理速度;使用在线困难样例挖掘,改善数据集样本不均衡问题,提升网络训练效果。实验结果表明,改进后的算法相较基线算法在分割准确率上提升了1.5%,在处理速度上提高了1.3%,兼具精度和速度优势。
