模拟研究京津冀地区地表土壤热通量,对该区域的干旱监测、生态系统功能评估、气候变化模拟及农作物估产具有重要参考意义.基于京津冀地区怀来站和馆陶站2个站点每10 min的自动气象站数据(四分量辐射、地表辐射温度、土壤热通量、多层土...模拟研究京津冀地区地表土壤热通量,对该区域的干旱监测、生态系统功能评估、气候变化模拟及农作物估产具有重要参考意义.基于京津冀地区怀来站和馆陶站2个站点每10 min的自动气象站数据(四分量辐射、地表辐射温度、土壤热通量、多层土壤水分和土壤温度),通过昼夜分开调整G_(0)_SEBS模型,构建了适用于京津冀地区的新模型G_(0)_SEBSadj.利用GLASS和GLDAS区域数据驱动G_(0)_SEBSadj模型,定量模拟了京津冀地区2010-2020年较高精度的地表土壤热通量(G_(0)),分析得出该区域近11 a G_(0)的时空变化特征.2010-2020年,京津冀地区G_(0)波动上升,这可能与全球气候变暖趋势密切相关;空间上,京津冀地区西北区域年均G_(0)较高,而太行山等海拔较高区域年均G_(0)较低,这可能与海拔地形影响相关.研究得出了京津冀地区2010-2020年地表土壤热通量的区域数据,揭示了该地区G_(0)的时空分布特征,为区域蒸散发及相关研究提供了数据支持及参考.展开更多
High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes an...High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.展开更多
现有的基于卷积神经网络的超分辨率重建方法由于感受野限制,难以充分利用遥感图像丰富的上下文信息和自相关性,导致重建效果不佳.针对该问题,本文提出了一种基于多重蒸馏与Transformer的遥感图像超分辨率(remote sensing image super-re...现有的基于卷积神经网络的超分辨率重建方法由于感受野限制,难以充分利用遥感图像丰富的上下文信息和自相关性,导致重建效果不佳.针对该问题,本文提出了一种基于多重蒸馏与Transformer的遥感图像超分辨率(remote sensing image super-resolution based on multi-distillation and Transformer,MDT)重建方法.首先结合多重蒸馏和双注意力机制,逐步提取低分辨率图像中的多尺度特征,以减少特征丢失.接着,构建一种卷积调制Transformer来提取图像的全局信息,恢复更多复杂的纹理细节,从而提升重建图像的视觉效果.最后,在上采样过程中添加全局残差路径,提高特征在网络中的传播效率,有效减少了图像的失真与伪影问题.在AID和UCMerced两个数据集上的进行实验,结果表明,本文方法在放大至4倍超分辨率任务上的峰值信噪比和结构相似度分别最高达到了29.10 dB和0.7807,重建图像质量明显提高,并且在细节保留方面达到了更好的视觉效果.展开更多
为加速AprilTag检测,提出了一种基于改进YOLOv5s预提取RoI(region of interest)的AprilTag检测方法。改进YOLOv5s网络,在输入灰度图像的单通道模式下,分别采用Ghost Bottleneck和ConvNeXt Block替换主干网络和颈部网络的C3和瓶颈模块,...为加速AprilTag检测,提出了一种基于改进YOLOv5s预提取RoI(region of interest)的AprilTag检测方法。改进YOLOv5s网络,在输入灰度图像的单通道模式下,分别采用Ghost Bottleneck和ConvNeXt Block替换主干网络和颈部网络的C3和瓶颈模块,提高模型的推理速度和泛化能力;通过亮度增强扩充数据集,提高模型鲁棒性。基于改进的YOLOv5网络进行AprilTag预识别,通过输出锚框划分RoI进行AprilTag检测,缩小图像处理范围,提高计算效率。实验结果表明,提出的AprilTag检测方法在1080P图像下FPS比传统AprilTag算法提高了77.42%以上。展开更多
文摘模拟研究京津冀地区地表土壤热通量,对该区域的干旱监测、生态系统功能评估、气候变化模拟及农作物估产具有重要参考意义.基于京津冀地区怀来站和馆陶站2个站点每10 min的自动气象站数据(四分量辐射、地表辐射温度、土壤热通量、多层土壤水分和土壤温度),通过昼夜分开调整G_(0)_SEBS模型,构建了适用于京津冀地区的新模型G_(0)_SEBSadj.利用GLASS和GLDAS区域数据驱动G_(0)_SEBSadj模型,定量模拟了京津冀地区2010-2020年较高精度的地表土壤热通量(G_(0)),分析得出该区域近11 a G_(0)的时空变化特征.2010-2020年,京津冀地区G_(0)波动上升,这可能与全球气候变暖趋势密切相关;空间上,京津冀地区西北区域年均G_(0)较高,而太行山等海拔较高区域年均G_(0)较低,这可能与海拔地形影响相关.研究得出了京津冀地区2010-2020年地表土壤热通量的区域数据,揭示了该地区G_(0)的时空分布特征,为区域蒸散发及相关研究提供了数据支持及参考.
基金provided by the Science Research Project of Hebei Education Department under grant No.BJK2024115.
文摘High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.
文摘现有的基于卷积神经网络的超分辨率重建方法由于感受野限制,难以充分利用遥感图像丰富的上下文信息和自相关性,导致重建效果不佳.针对该问题,本文提出了一种基于多重蒸馏与Transformer的遥感图像超分辨率(remote sensing image super-resolution based on multi-distillation and Transformer,MDT)重建方法.首先结合多重蒸馏和双注意力机制,逐步提取低分辨率图像中的多尺度特征,以减少特征丢失.接着,构建一种卷积调制Transformer来提取图像的全局信息,恢复更多复杂的纹理细节,从而提升重建图像的视觉效果.最后,在上采样过程中添加全局残差路径,提高特征在网络中的传播效率,有效减少了图像的失真与伪影问题.在AID和UCMerced两个数据集上的进行实验,结果表明,本文方法在放大至4倍超分辨率任务上的峰值信噪比和结构相似度分别最高达到了29.10 dB和0.7807,重建图像质量明显提高,并且在细节保留方面达到了更好的视觉效果.
文摘为加速AprilTag检测,提出了一种基于改进YOLOv5s预提取RoI(region of interest)的AprilTag检测方法。改进YOLOv5s网络,在输入灰度图像的单通道模式下,分别采用Ghost Bottleneck和ConvNeXt Block替换主干网络和颈部网络的C3和瓶颈模块,提高模型的推理速度和泛化能力;通过亮度增强扩充数据集,提高模型鲁棒性。基于改进的YOLOv5网络进行AprilTag预识别,通过输出锚框划分RoI进行AprilTag检测,缩小图像处理范围,提高计算效率。实验结果表明,提出的AprilTag检测方法在1080P图像下FPS比传统AprilTag算法提高了77.42%以上。
