The reverse time migration(RTM)of ground penetrating radar(GPR)is usually implemented in its two-dimensional(2D)form,due to huge computational cost.However,2D RTM algorithm is difficult to focus the scattering signal ...The reverse time migration(RTM)of ground penetrating radar(GPR)is usually implemented in its two-dimensional(2D)form,due to huge computational cost.However,2D RTM algorithm is difficult to focus the scattering signal and produce a high precision subsurface image when the object is buried in a complicated subsurface environment.To better handle the multi-off set GPR data,we propose a three-dimensional(3D)prestack RTM algorithm.The high-order fi nite diff erence time domian(FDTD)method,with the accuracy of eighth-order in space and second-order in time,is applied to simulate the forward and backward extrapolation electromagnetic fi elds.In addition,we use the normalized correlation imaging condition to obtain pre-stack RTM result and the Laplace fi lter to suppress the low frequency noise generated during the correlation process.The numerical test of 3D simulated GPR data demonstrated that 3D RTM image shows excellent coincidence with the true model.Compared with 2D RTM image,the 3D RTM image can more clearly and accurately refl ect the 3D spatial distribution of the target,and the resolution of the imaging results is far better.Furthermore,the application of observed GPR data further validates the eff ectiveness of the proposed 3D GPR RTM algorithm,and its fi nal image can more reliably guide the subsequent interpretation.展开更多
The use of unmanned aerial vehicles(UAV)for forest monitoring has grown significantly in recent years,providing information with high spatial resolution and temporal versatility.UAV with multispectral sensors allow th...The use of unmanned aerial vehicles(UAV)for forest monitoring has grown significantly in recent years,providing information with high spatial resolution and temporal versatility.UAV with multispectral sensors allow the use of indexes such as the normalized difference vegetation index(NDVI),which determines the vigor,physiological stress and photo synthetic activity of vegetation.This study aimed to analyze the spectral responses and variations of NDVI in tree crowns,as well as their correlation with climatic factors over the course of one year.The study area encompassed a 1.6-ha site in Durango,Mexico,where Pinus cembroides,Pinus engelmannii,and Quercus grisea coexist.Multispectral images were acquired with UAV and information on meteorological variables was obtained from NASA/POWER database.An ANOVA explored possible differences in NDVI among the three species.Pearson correlation was performed to identify the linear relationship between NDVI and meteorological variables.Significant differences in NDVI values were found at the genus level(Pinus and Quercus),possibly related to the physiological features of the species and their phenology.Quercus grisea had the lowest NDVI values throughout the year which may be attributed to its sensitivity to relative humidity and temperatures.Although the use of UAV with a multispectral sensor for NDVI monitoring allowed genera differentiation,in more complex forest analyses hyperspectral and LiDAR sensors should be integrated,as well other vegetation indexes be considered.展开更多
目的光场图像因其能够捕捉不同深度的场景细节信息,可以有效提升显著性检测的效果。然而,焦点堆栈图像虽然富含深度信息,但不同焦平面中存在的模糊干扰会降低光场显著性检测的性能。此外,现有的大多数方法都仅在显著性预测阶段考虑不同...目的光场图像因其能够捕捉不同深度的场景细节信息,可以有效提升显著性检测的效果。然而,焦点堆栈图像虽然富含深度信息,但不同焦平面中存在的模糊干扰会降低光场显著性检测的性能。此外,现有的大多数方法都仅在显著性预测阶段考虑不同图像特征的交互,导致不同特征的互补性利用不足。为了解决以上问题,提出一种融合多阶段差分特征的光场图像显著性检测网络,旨在提高光场图像中显著物体检测的准确性。方法提出一种基于多阶段自差分特征的焦点堆栈深度感知方法,以连续深度聚焦信息指导显著目标定位。提出一种多模态阶段融合方法,通过多模态差异约束捕获高精度的焦点堆栈聚焦区域,以实现焦点堆栈图像与全聚焦图像的多阶段特征融合,并利用焦点堆栈深度感知方法和多模态阶段融合方法的互补信息增强目标物体的可识别性。将两种方法引入编码阶段,实现特征的早期交互,缓解了特征利用率低的问题。结果实验在被广泛应用的DUTLF-FS(Dalian University of Technology Light Field Focal Stack)、HFUT-Lytro(Hefei University of Technology Lytro)和Lytro Illum数据集上与11种方法进行比较。在DUTLF-FS数据集中,相比FESNet模型,在不额外引入深度图线索的前提下,最大F指标相对提升0.2%;在HFUT-Lytro数据集中,相比FESNet模型,平均绝对误差相对降低12.9%;在Lytro Illum数据集中,相比LFTransNet模型,平均绝对误差相对降低22.2%。消融实验进一步证实了所设计的模块的有效性。结论本文提出的显著性检测模型能有效增强复杂场景中的显著区域特征,并抑制背景区域,能够准确地识别显著目标。展开更多
China has experienced a rapid urbanization during recent decades,strongly affecting vegetation dynamics in areas undergoing a transformation from rural to urban areas.At the same time,national greening policies have b...China has experienced a rapid urbanization during recent decades,strongly affecting vegetation dynamics in areas undergoing a transformation from rural to urban areas.At the same time,national greening policies have been implemented to promote urban sustainability and urban greening in China in recent years.However,it is unclear how urban greening compensates vegetation losses from urban expansion at national scale.Here,we use Moderate Resolution Imaging Spectroradiometer and Landsat satellite normalized difference vegetation index time series to study 974 major cities(urban area>20 km^(2))in China during 2000 to 2020 and develop an urban vegetation change typology including 5 types of vegetation dynamics(greening,browning,stable,reversal,and recovery).We document a rapid urban expansion associated with a browning in urban areas before 2011,followed by widespread regreening of the urban areas after 2011.This recovery in greenness was found in 63.45%of the cities,while 14.68%showed a continuous browning,and 8.13%a continuous greening.Our findings reveal to what extent,where,and when vegetation browning from urban expansion is balanced by urban greening in urban core areas,which may indicate that initial vegetation losses are offset by urban greening initiatives.展开更多
基金This work is supported by the National Natural Science Foundation of China(No.41604039,41604102,41764005,41574078)Guangxi Natural Science Foundation project(No.2020GXNSFAA159121,2016GXNSFBA380215).
文摘The reverse time migration(RTM)of ground penetrating radar(GPR)is usually implemented in its two-dimensional(2D)form,due to huge computational cost.However,2D RTM algorithm is difficult to focus the scattering signal and produce a high precision subsurface image when the object is buried in a complicated subsurface environment.To better handle the multi-off set GPR data,we propose a three-dimensional(3D)prestack RTM algorithm.The high-order fi nite diff erence time domian(FDTD)method,with the accuracy of eighth-order in space and second-order in time,is applied to simulate the forward and backward extrapolation electromagnetic fi elds.In addition,we use the normalized correlation imaging condition to obtain pre-stack RTM result and the Laplace fi lter to suppress the low frequency noise generated during the correlation process.The numerical test of 3D simulated GPR data demonstrated that 3D RTM image shows excellent coincidence with the true model.Compared with 2D RTM image,the 3D RTM image can more clearly and accurately refl ect the 3D spatial distribution of the target,and the resolution of the imaging results is far better.Furthermore,the application of observed GPR data further validates the eff ectiveness of the proposed 3D GPR RTM algorithm,and its fi nal image can more reliably guide the subsequent interpretation.
基金supported by the National Council of Science and Technology of Mexico(CONACyT),which provided financial support through scholarships for postgraduate studies to J.L.G.S.(815176)and M.R.C.(507523)。
文摘The use of unmanned aerial vehicles(UAV)for forest monitoring has grown significantly in recent years,providing information with high spatial resolution and temporal versatility.UAV with multispectral sensors allow the use of indexes such as the normalized difference vegetation index(NDVI),which determines the vigor,physiological stress and photo synthetic activity of vegetation.This study aimed to analyze the spectral responses and variations of NDVI in tree crowns,as well as their correlation with climatic factors over the course of one year.The study area encompassed a 1.6-ha site in Durango,Mexico,where Pinus cembroides,Pinus engelmannii,and Quercus grisea coexist.Multispectral images were acquired with UAV and information on meteorological variables was obtained from NASA/POWER database.An ANOVA explored possible differences in NDVI among the three species.Pearson correlation was performed to identify the linear relationship between NDVI and meteorological variables.Significant differences in NDVI values were found at the genus level(Pinus and Quercus),possibly related to the physiological features of the species and their phenology.Quercus grisea had the lowest NDVI values throughout the year which may be attributed to its sensitivity to relative humidity and temperatures.Although the use of UAV with a multispectral sensor for NDVI monitoring allowed genera differentiation,in more complex forest analyses hyperspectral and LiDAR sensors should be integrated,as well other vegetation indexes be considered.
文摘目的光场图像因其能够捕捉不同深度的场景细节信息,可以有效提升显著性检测的效果。然而,焦点堆栈图像虽然富含深度信息,但不同焦平面中存在的模糊干扰会降低光场显著性检测的性能。此外,现有的大多数方法都仅在显著性预测阶段考虑不同图像特征的交互,导致不同特征的互补性利用不足。为了解决以上问题,提出一种融合多阶段差分特征的光场图像显著性检测网络,旨在提高光场图像中显著物体检测的准确性。方法提出一种基于多阶段自差分特征的焦点堆栈深度感知方法,以连续深度聚焦信息指导显著目标定位。提出一种多模态阶段融合方法,通过多模态差异约束捕获高精度的焦点堆栈聚焦区域,以实现焦点堆栈图像与全聚焦图像的多阶段特征融合,并利用焦点堆栈深度感知方法和多模态阶段融合方法的互补信息增强目标物体的可识别性。将两种方法引入编码阶段,实现特征的早期交互,缓解了特征利用率低的问题。结果实验在被广泛应用的DUTLF-FS(Dalian University of Technology Light Field Focal Stack)、HFUT-Lytro(Hefei University of Technology Lytro)和Lytro Illum数据集上与11种方法进行比较。在DUTLF-FS数据集中,相比FESNet模型,在不额外引入深度图线索的前提下,最大F指标相对提升0.2%;在HFUT-Lytro数据集中,相比FESNet模型,平均绝对误差相对降低12.9%;在Lytro Illum数据集中,相比LFTransNet模型,平均绝对误差相对降低22.2%。消融实验进一步证实了所设计的模块的有效性。结论本文提出的显著性检测模型能有效增强复杂场景中的显著区域特征,并抑制背景区域,能够准确地识别显著目标。
基金supported by the China Scholarship Council(CSC,grant no.201904910835 to X.Z.)Independent Research Fund Denmark-DFF Sapere Aude(grant 9064-00049B to M.B.)the Villum Foundation through the project“Deep Learning and Remote Sensing for Unlocking Global Ecosystem Resource Dynamics”(DeReEco to R.F.).
文摘China has experienced a rapid urbanization during recent decades,strongly affecting vegetation dynamics in areas undergoing a transformation from rural to urban areas.At the same time,national greening policies have been implemented to promote urban sustainability and urban greening in China in recent years.However,it is unclear how urban greening compensates vegetation losses from urban expansion at national scale.Here,we use Moderate Resolution Imaging Spectroradiometer and Landsat satellite normalized difference vegetation index time series to study 974 major cities(urban area>20 km^(2))in China during 2000 to 2020 and develop an urban vegetation change typology including 5 types of vegetation dynamics(greening,browning,stable,reversal,and recovery).We document a rapid urban expansion associated with a browning in urban areas before 2011,followed by widespread regreening of the urban areas after 2011.This recovery in greenness was found in 63.45%of the cities,while 14.68%showed a continuous browning,and 8.13%a continuous greening.Our findings reveal to what extent,where,and when vegetation browning from urban expansion is balanced by urban greening in urban core areas,which may indicate that initial vegetation losses are offset by urban greening initiatives.
