Environmentalmonitoring systems based on remote sensing technology have a wider monitoringrange and longer timeliness, which makes them widely used in the detection andmanagement of pollution sources. However, haze we...Environmentalmonitoring systems based on remote sensing technology have a wider monitoringrange and longer timeliness, which makes them widely used in the detection andmanagement of pollution sources. However, haze weather conditions degrade image qualityand reduce the precision of environmental monitoring systems. To address this problem,this research proposes a remote sensing image dehazingmethod based on the atmosphericscattering model and a dark channel prior constrained network. The method consists ofa dehazing network, a dark channel information injection network (DCIIN), and a transmissionmap network. Within the dehazing network, the branch fusion module optimizesfeature weights to enhance the dehazing effect. By leveraging dark channel information,the DCIIN enables high-quality estimation of the atmospheric veil. To ensure the outputof the deep learning model aligns with physical laws, we reconstruct the haze image usingthe prediction results from the three networks. Subsequently, we apply the traditionalloss function and dark channel loss function between the reconstructed haze image and theoriginal haze image. This approach enhances interpretability and reliabilitywhile maintainingadherence to physical principles. Furthermore, the network is trained on a synthesizednon-homogeneous haze remote sensing dataset using dark channel information from cloudmaps. The experimental results show that the proposed network can achieve better imagedehazing on both synthetic and real remote sensing images with non-homogeneous hazedistribution. This research provides a new idea for solving the problem of decreased accuracyof environmental monitoring systems under haze weather conditions and has strongpracticability.展开更多
针对遥感图像建筑物的轮廓分割不完整、边界分割模糊和阴影干扰等导致的错误分割问题,提出一种基于VGG16的卷积块注意力深度可分离卷积U-Net网络(VGG16 Convolutional Block Attention and Deep Separable Convolution U-Net,VCDG-UNet...针对遥感图像建筑物的轮廓分割不完整、边界分割模糊和阴影干扰等导致的错误分割问题,提出一种基于VGG16的卷积块注意力深度可分离卷积U-Net网络(VGG16 Convolutional Block Attention and Deep Separable Convolution U-Net,VCDG-UNet)。为对建筑物特征进行提取,编码器部分模型以具有强大特征提取能力的VGG16作为骨干网络;解码器部分用深度可分离卷积代替普通卷积来减少参数量并融合不同尺度的特征;引入卷积块注意力模块(Convolutional Block Attention Module,CBAM)加入跳跃连接中,使其更有效地从不同尺度的图像中提取上下文信息并提高其对重要区域的关注度;为解决网络训练过程中的梯度消失问题,使用了高斯误差线性单元(Gaussian Error Linear Unit,GELU)。实验结果显示,改进后的网络在WHU和INRIA数据集上的平均交并比(mean Intersection over Union,mIoU)和F1-score分别达到了94.20%、96.83%和89.69%、94.51%,相较于基础模型高出了1.59%、0.76%和2.8%、1.59%。展开更多
Cloud detection is a critical preprocessing step in remote sensing image processing, as the presence of clouds significantly affects the accuracy of remote sensing data and limits its applicability across various doma...Cloud detection is a critical preprocessing step in remote sensing image processing, as the presence of clouds significantly affects the accuracy of remote sensing data and limits its applicability across various domains. This study presents an enhanced cloud detection method based on the U-Net architecture, designed to address the challenges of multi-scale cloud features and long-range dependencies inherent in remote sensing imagery. A Multi-Scale Dilated Attention (MSDA) module is introduced to effectively integrate multi-scale information and model long-range dependencies across different scales, enhancing the model’s ability to detect clouds of varying sizes. Additionally, a Multi-Head Self-Attention (MHSA) mechanism is incorporated to improve the model’s capacity for capturing finer details, particularly in distinguishing thin clouds from surface features. A multi-path supervision mechanism is also devised to ensure the model learns cloud features at multiple scales, further boosting the accuracy and robustness of cloud mask generation. Experimental results demonstrate that the enhanced model achieves superior performance compared to other benchmarked methods in complex scenarios. It significantly improves cloud detection accuracy, highlighting its strong potential for practical applications in cloud detection tasks.展开更多
Making the distinction between different plantation tree species is crucial for creating reliable and trustworthy information, which is critical in forestry administration and upkeep. Over the years, forest delineatio...Making the distinction between different plantation tree species is crucial for creating reliable and trustworthy information, which is critical in forestry administration and upkeep. Over the years, forest delineation and mapping have been done using the conventional techniques, such as the utilization of ground truth facts together with orthophotos. These techniques have been proven to be very precise, but they are expensive, cumbersome, and challenging to employ in remote regions. To resolve this shortfall, this research investigates the potential of data from the commercial, PlanetScope CubeSat and the freely available, Sentinel 2 data from Copernicus to discriminate commercial forest tree species in the Usutu Forest, Eswatini. Two approaches for image classification, Random Forest (RF) and the Support Vector Machine (SVM) were investigated at different levels of the forest database classification which is the genus (family of tree species) and species levels. The result of the study indicates that, the Sentinel 2 images had the highest species classification accuracy compared to the PlanetScope image. Both classification methods achieved a 94% maximum OA and 0.90 kappa value at the genus level with the Sentinel 2 imagery. At the species level, the Sentinel 2 imagery again showed highly acceptable results with the SVM method, with an OA of 82%. The PlanetScope images performed badly with less than 64% OA for both RF and SVM at the genus level and poorer at the species level with a low OA figure, 47% and 53% for the SVM and RF respectively. Our results suggest that the freely available Sentinel 2 data together with the SVM method has a high potential for identifying differences between commercial tree species than the PlanetScope. The study uncovered that both classification methods are highly capable of classifying species under the gum genus group (esmi, egxu, and egxn) using both imageries. However, it was difficult to separate species types under the pine genus group, particularly discriminating the hybrid species such as pech and pell since pech is a hybrid species for pell.展开更多
砾幕层是戈壁生态系统的重要组成部分,大尺度的砾幕层遥感监测对戈壁生态系统保护具有重要意义。针对砾幕层结构松散、异质性强的特点,本文提出了一种基于U-ConvHDNet语义分割的砾幕层自动信息制图方法,利用2023年8月的哈密全区域的Sent...砾幕层是戈壁生态系统的重要组成部分,大尺度的砾幕层遥感监测对戈壁生态系统保护具有重要意义。针对砾幕层结构松散、异质性强的特点,本文提出了一种基于U-ConvHDNet语义分割的砾幕层自动信息制图方法,利用2023年8月的哈密全区域的Sentinel-2影像提取戈壁砾幕层信息。结果表明,U-ConvHDNet模型的F1分数为0.918,优于参与对比的7个主流语义分割模型,消融试验表明骨架网络的改进与上下采样模块的联合使用有效提升了精度。双重感受野滑窗策略优化了拼接线附近不稳定的现象,提取出哈密戈壁砾幕层总面积为1.026×105 km 2,其信息提取精度的F1分数为0.921。本文研究可为戈壁砾幕层的监测和戈壁生态系统治理提供技术支撑。展开更多
基金supported by the National Natural Science Foundation of China(No.51605054).
文摘Environmentalmonitoring systems based on remote sensing technology have a wider monitoringrange and longer timeliness, which makes them widely used in the detection andmanagement of pollution sources. However, haze weather conditions degrade image qualityand reduce the precision of environmental monitoring systems. To address this problem,this research proposes a remote sensing image dehazingmethod based on the atmosphericscattering model and a dark channel prior constrained network. The method consists ofa dehazing network, a dark channel information injection network (DCIIN), and a transmissionmap network. Within the dehazing network, the branch fusion module optimizesfeature weights to enhance the dehazing effect. By leveraging dark channel information,the DCIIN enables high-quality estimation of the atmospheric veil. To ensure the outputof the deep learning model aligns with physical laws, we reconstruct the haze image usingthe prediction results from the three networks. Subsequently, we apply the traditionalloss function and dark channel loss function between the reconstructed haze image and theoriginal haze image. This approach enhances interpretability and reliabilitywhile maintainingadherence to physical principles. Furthermore, the network is trained on a synthesizednon-homogeneous haze remote sensing dataset using dark channel information from cloudmaps. The experimental results show that the proposed network can achieve better imagedehazing on both synthetic and real remote sensing images with non-homogeneous hazedistribution. This research provides a new idea for solving the problem of decreased accuracyof environmental monitoring systems under haze weather conditions and has strongpracticability.
文摘针对遥感图像建筑物的轮廓分割不完整、边界分割模糊和阴影干扰等导致的错误分割问题,提出一种基于VGG16的卷积块注意力深度可分离卷积U-Net网络(VGG16 Convolutional Block Attention and Deep Separable Convolution U-Net,VCDG-UNet)。为对建筑物特征进行提取,编码器部分模型以具有强大特征提取能力的VGG16作为骨干网络;解码器部分用深度可分离卷积代替普通卷积来减少参数量并融合不同尺度的特征;引入卷积块注意力模块(Convolutional Block Attention Module,CBAM)加入跳跃连接中,使其更有效地从不同尺度的图像中提取上下文信息并提高其对重要区域的关注度;为解决网络训练过程中的梯度消失问题,使用了高斯误差线性单元(Gaussian Error Linear Unit,GELU)。实验结果显示,改进后的网络在WHU和INRIA数据集上的平均交并比(mean Intersection over Union,mIoU)和F1-score分别达到了94.20%、96.83%和89.69%、94.51%,相较于基础模型高出了1.59%、0.76%和2.8%、1.59%。
文摘Cloud detection is a critical preprocessing step in remote sensing image processing, as the presence of clouds significantly affects the accuracy of remote sensing data and limits its applicability across various domains. This study presents an enhanced cloud detection method based on the U-Net architecture, designed to address the challenges of multi-scale cloud features and long-range dependencies inherent in remote sensing imagery. A Multi-Scale Dilated Attention (MSDA) module is introduced to effectively integrate multi-scale information and model long-range dependencies across different scales, enhancing the model’s ability to detect clouds of varying sizes. Additionally, a Multi-Head Self-Attention (MHSA) mechanism is incorporated to improve the model’s capacity for capturing finer details, particularly in distinguishing thin clouds from surface features. A multi-path supervision mechanism is also devised to ensure the model learns cloud features at multiple scales, further boosting the accuracy and robustness of cloud mask generation. Experimental results demonstrate that the enhanced model achieves superior performance compared to other benchmarked methods in complex scenarios. It significantly improves cloud detection accuracy, highlighting its strong potential for practical applications in cloud detection tasks.
文摘Making the distinction between different plantation tree species is crucial for creating reliable and trustworthy information, which is critical in forestry administration and upkeep. Over the years, forest delineation and mapping have been done using the conventional techniques, such as the utilization of ground truth facts together with orthophotos. These techniques have been proven to be very precise, but they are expensive, cumbersome, and challenging to employ in remote regions. To resolve this shortfall, this research investigates the potential of data from the commercial, PlanetScope CubeSat and the freely available, Sentinel 2 data from Copernicus to discriminate commercial forest tree species in the Usutu Forest, Eswatini. Two approaches for image classification, Random Forest (RF) and the Support Vector Machine (SVM) were investigated at different levels of the forest database classification which is the genus (family of tree species) and species levels. The result of the study indicates that, the Sentinel 2 images had the highest species classification accuracy compared to the PlanetScope image. Both classification methods achieved a 94% maximum OA and 0.90 kappa value at the genus level with the Sentinel 2 imagery. At the species level, the Sentinel 2 imagery again showed highly acceptable results with the SVM method, with an OA of 82%. The PlanetScope images performed badly with less than 64% OA for both RF and SVM at the genus level and poorer at the species level with a low OA figure, 47% and 53% for the SVM and RF respectively. Our results suggest that the freely available Sentinel 2 data together with the SVM method has a high potential for identifying differences between commercial tree species than the PlanetScope. The study uncovered that both classification methods are highly capable of classifying species under the gum genus group (esmi, egxu, and egxn) using both imageries. However, it was difficult to separate species types under the pine genus group, particularly discriminating the hybrid species such as pech and pell since pech is a hybrid species for pell.
文摘砾幕层是戈壁生态系统的重要组成部分,大尺度的砾幕层遥感监测对戈壁生态系统保护具有重要意义。针对砾幕层结构松散、异质性强的特点,本文提出了一种基于U-ConvHDNet语义分割的砾幕层自动信息制图方法,利用2023年8月的哈密全区域的Sentinel-2影像提取戈壁砾幕层信息。结果表明,U-ConvHDNet模型的F1分数为0.918,优于参与对比的7个主流语义分割模型,消融试验表明骨架网络的改进与上下采样模块的联合使用有效提升了精度。双重感受野滑窗策略优化了拼接线附近不稳定的现象,提取出哈密戈壁砾幕层总面积为1.026×105 km 2,其信息提取精度的F1分数为0.921。本文研究可为戈壁砾幕层的监测和戈壁生态系统治理提供技术支撑。
