Automatically detecting Ulva prolifera(U.prolifera)in rainy and cloudy weather using remote sensing imagery has been a long-standing problem.Here,we address this challenge by combining high-resolution Synthetic Apertu...Automatically detecting Ulva prolifera(U.prolifera)in rainy and cloudy weather using remote sensing imagery has been a long-standing problem.Here,we address this challenge by combining high-resolution Synthetic Aperture Radar(SAR)imagery with the machine learning,and detect the U.prolifera of the South Yellow Sea of China(SYS)in 2021.The findings indicate that the Random Forest model can accurately and robustly detect U.prolifera,even in the presence of complex ocean backgrounds and speckle noise.Visual inspection confirmed that the method successfully identified the majority of pixels containing U.prolifera without misidentify-ing noise pixels or seawater pixels as U.prolifera.Additionally,the method demonstrated consistent performance across different im-ages,with an average Area Under Curve(AUC)of 0.930(+0.028).The analysis yielded an overall accuracy of over 96%,with an aver-age Kappa coefficient of 0.941(+0.038).Compared to the traditional thresholding method,Random Forest model has a lower estima-tion error of 14.81%.Practical application indicates that this method can be used in the detection of unprecedented U.prolifera in 2021 to derive continuous spatiotemporal changes.This study provides a potential new method to detect U.prolifera and enhances our under-standing of macroalgal outbreaks in the marine environment.展开更多
Satellite remote sensing of inland water body requires a high spatial resolution and a multiband narrow spectral resolution, which makes the fusion between panchromatic(PAN) and multi-spectral(MS) images particularly ...Satellite remote sensing of inland water body requires a high spatial resolution and a multiband narrow spectral resolution, which makes the fusion between panchromatic(PAN) and multi-spectral(MS) images particularly important. Taking the Daquekou section of the Qiantang River as an observation target, four conventional fusion methods widely accepted in satellite image processing, including pan sharpening(PS), principal component analysis(PCA), Gram-Schmidt(GS), and wavelet fusion(WF), are utilized to fuse MS and PAN images of GF-1.The results of subjective and objective evaluation methods application indicate that GS performs the best,followed by the PCA, the WF and the PS in the order of descending. The existence of a large area of the water body is a dominant factor impacting the fusion performance. Meanwhile, the ability of retaining spatial and spectral informations is an important factor affecting the fusion performance of different fusion methods. The fundamental difference of reflectivity information acquisition between water and land is the reason for the failure of conventional fusion methods for land observation such as the PS to be used in the presence of the large water body. It is suggested that the adoption of the conventional fusion methods in the observing water body as the main target should be taken with caution. The performances of the fusion methods need re-assessment when the large-scale water body is present in the remote sensing image or when the research aims for the water body observation.展开更多
The Staring Area Imaging Technology(SAIT) satellite continuously "images" the target over a certain time range, and can realize continuous imaging and multi-angle imaging of the area of interest. It has the ...The Staring Area Imaging Technology(SAIT) satellite continuously "images" the target over a certain time range, and can realize continuous imaging and multi-angle imaging of the area of interest. It has the characteristics of flexible imaging parameter setting and fast image preprocessing speed, enabling dynamic target detection and tracking, super-resolution, surface 3 D model construction, night-time imaging and many other application tasks. Based on the technical characteristics of the SAIT satellite, this paper analyzes the challenges in satellite development and data processing, focuses on the quasi-realtime application of SAIT satellite data, and looks at the development trend of the SAIT satellite.展开更多
Summer floods occur frequently in many regions of China,affecting economic development and social stability.Remote sensing is a new technique in disaster monitoring.In this study,the Sihu Basin in Hubei Province of Ch...Summer floods occur frequently in many regions of China,affecting economic development and social stability.Remote sensing is a new technique in disaster monitoring.In this study,the Sihu Basin in Hubei Province of China and the Huaibei Plain in Anhui Province of China were selected as the study areas.Thresholds of backscattering coefficients in the decision tree method were calculated with the histogram analysis method,and flood disaster monitoring in the two study areas was conducted with the threshold method using Sentinel-1 satellite images.Through satellite-based flood disaster monitoring,the flooded maps and the areas of expanded water bodies and flooded crops were derived.The satellite-based monitoring maps were derived by comparing the expanded area of images during a flood disaster with that before the disaster.The difference in spatiotemporal distribution of flood disasters in these two regions was analyzed.The results showed that flood disasters in the Sihu Basin occurred frequently in June and July,and flood disasters in the Huaibei Plain mostly occurred in August,with a high interannual vari-ability.Flood disasters in the Sihu Basin were usually widespread,and the affected area was between Changhu and Honghu lakes.The Huaibei Plain was affected by scattered disasters.The annual mean percentages of flooded crop area were 14.91%and 3.74% in the Sihu Basin and Huaibei Plain,respectively.The accuracies of the extracted flooded area in the Sihu Basin in 2016 and 2017 were 96.20% and 95.19%,respectively.展开更多
Accurately locating and studying grounding lines is essential for predicting the response of glaciers to climate change.However,it is challenging tofind grounding lines since they are subglacial features.In this study...Accurately locating and studying grounding lines is essential for predicting the response of glaciers to climate change.However,it is challenging tofind grounding lines since they are subglacial features.In this study,Sentinel-1 synthetic aperture radar(SAR)data were utilized to derive the grounding lines of the Riiser-Larsen Ice Shelf.A new method with inspiration drawn from multi-temporal baseline InSAR techniques is proposed.It takes advantage of the temporal consistency of the vertical displacement gradients and identifies grounding zones pixel-by-pixel on a stack of double differential interferograms,thereby providing grounding line proxies.As it fully exploits coherent signals in both spatial and temporal domains,the maximum possible number of grounding zone pixels can be obtained.Moreover,due to the introduction of the concept of the temporal consistency,the method can cope with short term grounding linefluctuations to some extent and may mitigate the influences of atmospheric disturbances and residual ice displacements.The resulting grounding lines are compared with the MEaSUREs Antarctic grounding line product.The comparison confirms the effectiveness of the proposed method and corroborates that the Riiser-Larsen Ice Shelf should have not undergone significant changes over the past few decades.展开更多
2013年4月成功发射的GF-1卫星是中国高分系列卫星的首发星,影像在中国农情遥感监测业务中得到了广泛应用,已成为大宗农作物种植面积遥感监测的主要数据源之一。高精度几何位置的配准是卫星农情定量化应用的基础与前提,该文提出了一种基...2013年4月成功发射的GF-1卫星是中国高分系列卫星的首发星,影像在中国农情遥感监测业务中得到了广泛应用,已成为大宗农作物种植面积遥感监测的主要数据源之一。高精度几何位置的配准是卫星农情定量化应用的基础与前提,该文提出了一种基于区域网平差方法修正GF-1卫星WFV(wide field view,WFV)影像RPC(rational polynomial coefficients,RPC)参数,获取更高几何定位精度的校正方法,形成了模式化的业务处理流程,为该影像在农情遥感监测中的应用奠定了基础。算法流程包括2个部分,首先是基于像面间仿射变换关系及有理多项式RFM(rational function model,RFM)模型构建轨道间的区域网平差数学模型,其次是根据影像连接点及少量控制点输入求解所有参与平差的卫星影像定向参数,获取亚像元级的校正结果。平差参数的解算是通过两步求解完成的,初始平差参数是根据连接点及对应的DEM高程值进行平差迭代至收敛,结果平差参数是将初始平差参数作为初始值代入区域网平差模型,并以逐点消元方式约化法方程,解算出各影像的仿射变换参数。该文在求解平差参数过程中,直接使用DEM(digital elevation model)上获取的高程值作为约束条件,消除了平面坐标与高程的相关性,保证了区域网平差模型能够解算。混合地形、平原、山区3种情况下区域网平差结果表明,全连接点平差结果具有较高的相对定位精度,其行方向的中误差分别为0.3046、0.4674、0.3365像元,列方向的中误差分别为0.3677、0.2849、0.2889像元;而结合少量控制点的区域网平差则同时具有很高的绝对定位精度,其行方向的中误差分别为0.3648、0.5041、0.3605像元,列方向的中误差分别为0.4954、0.4039、0.6323像元,整体达到了亚像素级。最后,在农业应用基础控制底图的支持下,分别对原始影像、RPC校正影像、区域网平差后的影像进行几何配准,分析不同输入影像条件下的几何校正精度,仅有区域网平差后的影像达到了亚像元的校正精度,混合地形、平原、山区3种情况下行方向的中误差分别为0.6857、0.6664、1.0646像元,列方向的均方差分别为0.4342、0.4696、0.5609像元,但与几何校正前精度相比没有明显改善,说明本文提出的研究方法可以实现少量控制点条件下的几何精校正。不同DEM校正结果表明,对于山区,更高分辨率的DEM可以获得更好的定位精度。上述研究充分表明,该方法对GF-1/WFV数据的处理有效且可行,并在农业部中国农情遥感业务工作中得到了初步应用。展开更多
基金Under the auspices of National Natural Science Foundation of China(No.42071385)National Science and Technology Major Project of High Resolution Earth Observation System(No.79-Y50-G18-9001-22/23)。
文摘Automatically detecting Ulva prolifera(U.prolifera)in rainy and cloudy weather using remote sensing imagery has been a long-standing problem.Here,we address this challenge by combining high-resolution Synthetic Aperture Radar(SAR)imagery with the machine learning,and detect the U.prolifera of the South Yellow Sea of China(SYS)in 2021.The findings indicate that the Random Forest model can accurately and robustly detect U.prolifera,even in the presence of complex ocean backgrounds and speckle noise.Visual inspection confirmed that the method successfully identified the majority of pixels containing U.prolifera without misidentify-ing noise pixels or seawater pixels as U.prolifera.Additionally,the method demonstrated consistent performance across different im-ages,with an average Area Under Curve(AUC)of 0.930(+0.028).The analysis yielded an overall accuracy of over 96%,with an aver-age Kappa coefficient of 0.941(+0.038).Compared to the traditional thresholding method,Random Forest model has a lower estima-tion error of 14.81%.Practical application indicates that this method can be used in the detection of unprecedented U.prolifera in 2021 to derive continuous spatiotemporal changes.This study provides a potential new method to detect U.prolifera and enhances our under-standing of macroalgal outbreaks in the marine environment.
基金The National Key Research and Development Program of China under contract Nos 2016YFC1400901 and 2018YFC1406600the National Natural Science Foundation of China under contract No.40706057+1 种基金the Environmental Protection and Science and Technology Plan Project of Zhejiang Province of China under contract No.2013A021the Research Center for Air Pollution and Health of Zhejiang University
文摘Satellite remote sensing of inland water body requires a high spatial resolution and a multiband narrow spectral resolution, which makes the fusion between panchromatic(PAN) and multi-spectral(MS) images particularly important. Taking the Daquekou section of the Qiantang River as an observation target, four conventional fusion methods widely accepted in satellite image processing, including pan sharpening(PS), principal component analysis(PCA), Gram-Schmidt(GS), and wavelet fusion(WF), are utilized to fuse MS and PAN images of GF-1.The results of subjective and objective evaluation methods application indicate that GS performs the best,followed by the PCA, the WF and the PS in the order of descending. The existence of a large area of the water body is a dominant factor impacting the fusion performance. Meanwhile, the ability of retaining spatial and spectral informations is an important factor affecting the fusion performance of different fusion methods. The fundamental difference of reflectivity information acquisition between water and land is the reason for the failure of conventional fusion methods for land observation such as the PS to be used in the presence of the large water body. It is suggested that the adoption of the conventional fusion methods in the observing water body as the main target should be taken with caution. The performances of the fusion methods need re-assessment when the large-scale water body is present in the remote sensing image or when the research aims for the water body observation.
文摘The Staring Area Imaging Technology(SAIT) satellite continuously "images" the target over a certain time range, and can realize continuous imaging and multi-angle imaging of the area of interest. It has the characteristics of flexible imaging parameter setting and fast image preprocessing speed, enabling dynamic target detection and tracking, super-resolution, surface 3 D model construction, night-time imaging and many other application tasks. Based on the technical characteristics of the SAIT satellite, this paper analyzes the challenges in satellite development and data processing, focuses on the quasi-realtime application of SAIT satellite data, and looks at the development trend of the SAIT satellite.
基金This work was supported by the National Key Research and Development Program of China(Grants No.2018YFC1508302 and 2018YFC1508301)the Natural Science Foundation of Hubei Province of China(Grant No.2019CFB507).
文摘Summer floods occur frequently in many regions of China,affecting economic development and social stability.Remote sensing is a new technique in disaster monitoring.In this study,the Sihu Basin in Hubei Province of China and the Huaibei Plain in Anhui Province of China were selected as the study areas.Thresholds of backscattering coefficients in the decision tree method were calculated with the histogram analysis method,and flood disaster monitoring in the two study areas was conducted with the threshold method using Sentinel-1 satellite images.Through satellite-based flood disaster monitoring,the flooded maps and the areas of expanded water bodies and flooded crops were derived.The satellite-based monitoring maps were derived by comparing the expanded area of images during a flood disaster with that before the disaster.The difference in spatiotemporal distribution of flood disasters in these two regions was analyzed.The results showed that flood disasters in the Sihu Basin occurred frequently in June and July,and flood disasters in the Huaibei Plain mostly occurred in August,with a high interannual vari-ability.Flood disasters in the Sihu Basin were usually widespread,and the affected area was between Changhu and Honghu lakes.The Huaibei Plain was affected by scattered disasters.The annual mean percentages of flooded crop area were 14.91%and 3.74% in the Sihu Basin and Huaibei Plain,respectively.The accuracies of the extracted flooded area in the Sihu Basin in 2016 and 2017 were 96.20% and 95.19%,respectively.
基金funded by the National Natural Science Foundation of China(Grant No.42074034).
文摘Accurately locating and studying grounding lines is essential for predicting the response of glaciers to climate change.However,it is challenging tofind grounding lines since they are subglacial features.In this study,Sentinel-1 synthetic aperture radar(SAR)data were utilized to derive the grounding lines of the Riiser-Larsen Ice Shelf.A new method with inspiration drawn from multi-temporal baseline InSAR techniques is proposed.It takes advantage of the temporal consistency of the vertical displacement gradients and identifies grounding zones pixel-by-pixel on a stack of double differential interferograms,thereby providing grounding line proxies.As it fully exploits coherent signals in both spatial and temporal domains,the maximum possible number of grounding zone pixels can be obtained.Moreover,due to the introduction of the concept of the temporal consistency,the method can cope with short term grounding linefluctuations to some extent and may mitigate the influences of atmospheric disturbances and residual ice displacements.The resulting grounding lines are compared with the MEaSUREs Antarctic grounding line product.The comparison confirms the effectiveness of the proposed method and corroborates that the Riiser-Larsen Ice Shelf should have not undergone significant changes over the past few decades.
文摘2013年4月成功发射的GF-1卫星是中国高分系列卫星的首发星,影像在中国农情遥感监测业务中得到了广泛应用,已成为大宗农作物种植面积遥感监测的主要数据源之一。高精度几何位置的配准是卫星农情定量化应用的基础与前提,该文提出了一种基于区域网平差方法修正GF-1卫星WFV(wide field view,WFV)影像RPC(rational polynomial coefficients,RPC)参数,获取更高几何定位精度的校正方法,形成了模式化的业务处理流程,为该影像在农情遥感监测中的应用奠定了基础。算法流程包括2个部分,首先是基于像面间仿射变换关系及有理多项式RFM(rational function model,RFM)模型构建轨道间的区域网平差数学模型,其次是根据影像连接点及少量控制点输入求解所有参与平差的卫星影像定向参数,获取亚像元级的校正结果。平差参数的解算是通过两步求解完成的,初始平差参数是根据连接点及对应的DEM高程值进行平差迭代至收敛,结果平差参数是将初始平差参数作为初始值代入区域网平差模型,并以逐点消元方式约化法方程,解算出各影像的仿射变换参数。该文在求解平差参数过程中,直接使用DEM(digital elevation model)上获取的高程值作为约束条件,消除了平面坐标与高程的相关性,保证了区域网平差模型能够解算。混合地形、平原、山区3种情况下区域网平差结果表明,全连接点平差结果具有较高的相对定位精度,其行方向的中误差分别为0.3046、0.4674、0.3365像元,列方向的中误差分别为0.3677、0.2849、0.2889像元;而结合少量控制点的区域网平差则同时具有很高的绝对定位精度,其行方向的中误差分别为0.3648、0.5041、0.3605像元,列方向的中误差分别为0.4954、0.4039、0.6323像元,整体达到了亚像素级。最后,在农业应用基础控制底图的支持下,分别对原始影像、RPC校正影像、区域网平差后的影像进行几何配准,分析不同输入影像条件下的几何校正精度,仅有区域网平差后的影像达到了亚像元的校正精度,混合地形、平原、山区3种情况下行方向的中误差分别为0.6857、0.6664、1.0646像元,列方向的均方差分别为0.4342、0.4696、0.5609像元,但与几何校正前精度相比没有明显改善,说明本文提出的研究方法可以实现少量控制点条件下的几何精校正。不同DEM校正结果表明,对于山区,更高分辨率的DEM可以获得更好的定位精度。上述研究充分表明,该方法对GF-1/WFV数据的处理有效且可行,并在农业部中国农情遥感业务工作中得到了初步应用。
