Background:Determining the appropriate window size is a critical step in the estimation process of stand structural variables based on remote sensing data.Because the value of the reference laser and image metrics tha...Background:Determining the appropriate window size is a critical step in the estimation process of stand structural variables based on remote sensing data.Because the value of the reference laser and image metrics that afect the quality of the prediction model depends on window size.However,suitable window sizes are usually determined by trial and error.There are a limited number of published studies evaluating appropriate window sizes for diferent remote sensing data.This research investigated the efect of window size on predicting forest structural variables using airborne LiDAR data,digital aerial image and WorldView-3 satellite image.Results:In the WorldView-3 and digital aerial image,signifcant diferences were observed in the prediction accuracies of the structural variables according to diferent window sizes.For the estimation based on WorldView-3 in black pine stands,the optimal window sizes for stem number(N),volume(V),basal area(BA)and mean height(H)were determined as 1000 m^(2),100 m^(2),100 m^(2) and 600 m^(2),respectively.In oak stands,the R^(2) values of each moving window size were almost identical for N and BA.The optimal window size was 400 m^(2) for V and 600 m^(2) for H.For the estimation based on aerial image in black pine stands,the 800 m^(2) window size was optimal for N and H,the 600 m^(2) window size was optimal for V and the 1000 m^(2) window size was optimal for BA.In the oak stands,the optimal window sizes for N,V,BA and H were determined as 1000 m^(2),100 m^(2),100 m^(2) and 600 m^(2),respectively.The optimal window sizes may need to be scaled up or down to match the stand canopy components.In the LiDAR data,the R^(2) values of each window size were almost identical for all variables of the black pine and the oak stands.Conclusion:This study illustrated that the window size has an efect on the prediction accuracy in estimating forest structural variables based on remote sensing data.Moreover,the results showed that the optimal window size for forest structural variables varies according to remote sensing data and tree species composition.展开更多
Windowing applied to a given signal is a technique commonly used in signal processing in order to reduce spectral leakage in a signal with many data. Several windows are well known: hamming, hanning, beartlett, etc. T...Windowing applied to a given signal is a technique commonly used in signal processing in order to reduce spectral leakage in a signal with many data. Several windows are well known: hamming, hanning, beartlett, etc. The selection of a window is based on its spectral characteristics. Several papers that analyze the amplitude and width of the lobes that appear in the spectrum of various types of window have been published. This is very important because the lobes can hide information on the frequency components of the original signal, in particular when frequency components are very close to each other. In this paper it is shown that the size of the window can also have an impact in the spectral information. Until today, the size of a window has been chosen in a subjective way. As far as we know, there are no publications that show how to determine the minimum size of a window. In this work the frequency interval between two consecutive values of a Fourier Transform is considered. This interval determines if the sampling frequency and the number of samples are adequate to differentiate between two frequency components that are very close. From the analysis of this interval, a mathematical inequality is obtained, that determines in an objective way, the minimum size of a window. Two examples of the use of this criterion are presented. The results show that the hiding of information of a signal is due mainly to the wrong choice of the size of the window, but also to the relative amplitude of the frequency components and the type of window. Windowing is the main tool used in spectral analysis with nonparametric periodograms. Until now, optimization was based on the type of window. In this paper we show that the right choice of the size of a window assures on one hand that the number of data is enough to resolve the frequencies involved in the signal, and on the other, reduces the number of required data, and thus the processing time, when very long files are being analyzed.展开更多
Daylighting studies in buildings are key parts of environmental analysis and can be easily conducted at the early stages of design as part of environmentally responsive building design as well as to inform the final a...Daylighting studies in buildings are key parts of environmental analysis and can be easily conducted at the early stages of design as part of environmentally responsive building design as well as to inform the final architectural layout and interior design. The main aim of this study is to demonstrate how such daylighting studies can be completed at the early stages of design and, at the same time, to show the impact of window design and positioning on building indoor environments. The paper is focused on a study of window influence on room daylighting in residential buildings and computer lighting simulations in software packages： Windows Daylighting System and Autodesk Ecotect Analysis, have been carried out for different style and positioning of windows using several case studies. The main findings clearly indicated that not only the window size and style matters, but also the positioning of windows considering external walls which would make a significant influence on room daylighting levels and, therefore, such daylight studies are very important for the early stage of environmental analysis during building design.展开更多
The recovery of coseismic 3-dimensional(3D)surface deformation field plays a crucial role in studying seismic source characteristics and earthquake hazards.As of now,there are two main types of methods for recovering ...The recovery of coseismic 3-dimensional(3D)surface deformation field plays a crucial role in studying seismic source characteristics and earthquake hazards.As of now,there are two main types of methods for recovering coseismic 3D surface deformations:the first type is to solve the problem directly by using the least squares method based on observations from three or more viewpoints,and the second type solves the problem by combining ascending and descending In SAR line-of-sight(LOS)observations with constraint models.The former type is mainly applicable to surface rupture earthquakes,because when an earthquake ruptures to the surface,we can usually obtain surface deformation observations from three or more views.The latter type applies to earthquakes that do not cause surface ruptures and have extensive blind faults.Currently,most research focuses on improving the above types of methods.However,some key factors in the coseismic 3D surface deformation inversion are rarely mentioned,such as the influence of window size on the inversion results in the strain model and variance component estimation method(SM-VCE),and whether the outliers in the observational data are considered.So,we developed a new chain of integrating In SAR observation and SM-VCE model to systematically assess the impacts of window size and outliers on coseismic 3D surface deformation inversions.Through simulation experiments,we observed that the selection of window size significantly impacts the accuracy of the results.Specifically,larger window sizes lead to wider residuals ranges in the fault region,resulting in the loss of extreme solution values when using a window of 15×15 pixels(i.e.,7.5 km×7.5 km).Hence,we recommend utilizing windows with 7×7 pixels or 9×9 pixels for optimal accuracy,as larger window sizes diminish the significance of the outcomes.The elimination of points displaying different deformation directions helps reduce residuals and preserves near-field deformation results.Furthermore,the residuals along the 3D direction can be reduced by 10%-30%when a small set of points is selected using a method based on Euclidean distances.However,when 441 points were selected,the vertical residuals increased by 22%compared to 81 points.Integration of the SM-VCE algorithm with robust estimation techniques effectively minimizes far-field deformation errors in data,thereby marginally enhancing the near-field deformation solution.展开更多
针对自适应中值滤波在窗口迭代过程中存在像素点重复参与运算导致算法复杂度较高的问题,提出了一种改进的中值滤波算法.首先依据有效像素点与窗口中心点间的坐标距离来快速确定最佳滤波窗口尺寸,避免了窗口迭代造成的像素点重复排序;之...针对自适应中值滤波在窗口迭代过程中存在像素点重复参与运算导致算法复杂度较高的问题,提出了一种改进的中值滤波算法.首先依据有效像素点与窗口中心点间的坐标距离来快速确定最佳滤波窗口尺寸,避免了窗口迭代造成的像素点重复排序;之后对窗口内的有效像素点进行取中值操作,有效削弱了噪声点的干扰,进一步提升了图像滤波的质量.经实验验证,与自适应中值滤波算法比较,复杂度显著降低,峰值信噪比(peak signal to noise ratio,PSNR)值平均提高10 d B左右.和同类文献比较在算法复杂度和图像降噪效果间做出了一个较佳的权衡.最后将该算法应用于Kinect深度图降噪上获得了不错的效果.展开更多
文摘Background:Determining the appropriate window size is a critical step in the estimation process of stand structural variables based on remote sensing data.Because the value of the reference laser and image metrics that afect the quality of the prediction model depends on window size.However,suitable window sizes are usually determined by trial and error.There are a limited number of published studies evaluating appropriate window sizes for diferent remote sensing data.This research investigated the efect of window size on predicting forest structural variables using airborne LiDAR data,digital aerial image and WorldView-3 satellite image.Results:In the WorldView-3 and digital aerial image,signifcant diferences were observed in the prediction accuracies of the structural variables according to diferent window sizes.For the estimation based on WorldView-3 in black pine stands,the optimal window sizes for stem number(N),volume(V),basal area(BA)and mean height(H)were determined as 1000 m^(2),100 m^(2),100 m^(2) and 600 m^(2),respectively.In oak stands,the R^(2) values of each moving window size were almost identical for N and BA.The optimal window size was 400 m^(2) for V and 600 m^(2) for H.For the estimation based on aerial image in black pine stands,the 800 m^(2) window size was optimal for N and H,the 600 m^(2) window size was optimal for V and the 1000 m^(2) window size was optimal for BA.In the oak stands,the optimal window sizes for N,V,BA and H were determined as 1000 m^(2),100 m^(2),100 m^(2) and 600 m^(2),respectively.The optimal window sizes may need to be scaled up or down to match the stand canopy components.In the LiDAR data,the R^(2) values of each window size were almost identical for all variables of the black pine and the oak stands.Conclusion:This study illustrated that the window size has an efect on the prediction accuracy in estimating forest structural variables based on remote sensing data.Moreover,the results showed that the optimal window size for forest structural variables varies according to remote sensing data and tree species composition.
文摘Windowing applied to a given signal is a technique commonly used in signal processing in order to reduce spectral leakage in a signal with many data. Several windows are well known: hamming, hanning, beartlett, etc. The selection of a window is based on its spectral characteristics. Several papers that analyze the amplitude and width of the lobes that appear in the spectrum of various types of window have been published. This is very important because the lobes can hide information on the frequency components of the original signal, in particular when frequency components are very close to each other. In this paper it is shown that the size of the window can also have an impact in the spectral information. Until today, the size of a window has been chosen in a subjective way. As far as we know, there are no publications that show how to determine the minimum size of a window. In this work the frequency interval between two consecutive values of a Fourier Transform is considered. This interval determines if the sampling frequency and the number of samples are adequate to differentiate between two frequency components that are very close. From the analysis of this interval, a mathematical inequality is obtained, that determines in an objective way, the minimum size of a window. Two examples of the use of this criterion are presented. The results show that the hiding of information of a signal is due mainly to the wrong choice of the size of the window, but also to the relative amplitude of the frequency components and the type of window. Windowing is the main tool used in spectral analysis with nonparametric periodograms. Until now, optimization was based on the type of window. In this paper we show that the right choice of the size of a window assures on one hand that the number of data is enough to resolve the frequencies involved in the signal, and on the other, reduces the number of required data, and thus the processing time, when very long files are being analyzed.
文摘Daylighting studies in buildings are key parts of environmental analysis and can be easily conducted at the early stages of design as part of environmentally responsive building design as well as to inform the final architectural layout and interior design. The main aim of this study is to demonstrate how such daylighting studies can be completed at the early stages of design and, at the same time, to show the impact of window design and positioning on building indoor environments. The paper is focused on a study of window influence on room daylighting in residential buildings and computer lighting simulations in software packages： Windows Daylighting System and Autodesk Ecotect Analysis, have been carried out for different style and positioning of windows using several case studies. The main findings clearly indicated that not only the window size and style matters, but also the positioning of windows considering external walls which would make a significant influence on room daylighting levels and, therefore, such daylight studies are very important for the early stage of environmental analysis during building design.
基金supported by the National Natural Science of Foundation of China(42104008,42204006)the Jiangxi Provincial Natural Science Foundation(20232BAB213075)the Open Fund of Hubei Luojia Laboratory(grant 230100019,230100020)。
文摘The recovery of coseismic 3-dimensional(3D)surface deformation field plays a crucial role in studying seismic source characteristics and earthquake hazards.As of now,there are two main types of methods for recovering coseismic 3D surface deformations:the first type is to solve the problem directly by using the least squares method based on observations from three or more viewpoints,and the second type solves the problem by combining ascending and descending In SAR line-of-sight(LOS)observations with constraint models.The former type is mainly applicable to surface rupture earthquakes,because when an earthquake ruptures to the surface,we can usually obtain surface deformation observations from three or more views.The latter type applies to earthquakes that do not cause surface ruptures and have extensive blind faults.Currently,most research focuses on improving the above types of methods.However,some key factors in the coseismic 3D surface deformation inversion are rarely mentioned,such as the influence of window size on the inversion results in the strain model and variance component estimation method(SM-VCE),and whether the outliers in the observational data are considered.So,we developed a new chain of integrating In SAR observation and SM-VCE model to systematically assess the impacts of window size and outliers on coseismic 3D surface deformation inversions.Through simulation experiments,we observed that the selection of window size significantly impacts the accuracy of the results.Specifically,larger window sizes lead to wider residuals ranges in the fault region,resulting in the loss of extreme solution values when using a window of 15×15 pixels(i.e.,7.5 km×7.5 km).Hence,we recommend utilizing windows with 7×7 pixels or 9×9 pixels for optimal accuracy,as larger window sizes diminish the significance of the outcomes.The elimination of points displaying different deformation directions helps reduce residuals and preserves near-field deformation results.Furthermore,the residuals along the 3D direction can be reduced by 10%-30%when a small set of points is selected using a method based on Euclidean distances.However,when 441 points were selected,the vertical residuals increased by 22%compared to 81 points.Integration of the SM-VCE algorithm with robust estimation techniques effectively minimizes far-field deformation errors in data,thereby marginally enhancing the near-field deformation solution.
文摘针对自适应中值滤波在窗口迭代过程中存在像素点重复参与运算导致算法复杂度较高的问题,提出了一种改进的中值滤波算法.首先依据有效像素点与窗口中心点间的坐标距离来快速确定最佳滤波窗口尺寸,避免了窗口迭代造成的像素点重复排序;之后对窗口内的有效像素点进行取中值操作,有效削弱了噪声点的干扰,进一步提升了图像滤波的质量.经实验验证,与自适应中值滤波算法比较,复杂度显著降低,峰值信噪比(peak signal to noise ratio,PSNR)值平均提高10 d B左右.和同类文献比较在算法复杂度和图像降噪效果间做出了一个较佳的权衡.最后将该算法应用于Kinect深度图降噪上获得了不错的效果.
