Pedo-spectroscopy has the potential to provide valuable information about soil physical,chemical,and biological properties.Nowadays,wemay predict soil properties usingVNIRfield imaging spectra(IS)such as Prisma satell...Pedo-spectroscopy has the potential to provide valuable information about soil physical,chemical,and biological properties.Nowadays,wemay predict soil properties usingVNIRfield imaging spectra(IS)such as Prisma satellite data or laboratory spectra(LS).The primary goal of this study is to investigate machine learning models namely Partial Least Squares Regression(PLSR)and Support Vector Regression(SVR)for the prediction of several soil properties,including clay,sand,silt,organic matter,nitrate NO3-,and calcium carbonate CaCO_(3),using five VNIR spectra dataset combinations(%IS,%LS)as follows:C1(0%IS,100%LS),C2(20%IS,80%LS),C3(50%IS,50%LS),C4(80%IS,20%LS)and C5(100%IS,0%LS).Soil samples were collected at bare soils and at the upper(0–30 cm)layer.The data set has been split into a training dataset 80%of the collected data(n=248)and a validation dataset 20%of the collected data(n=61).The proposed PLSR and SVR models were trained then tested for each dataset combination.According to our results,SVR outperforms PLSR for both:C1(0%IS,100%LS)and C5(100%IS,0%LS).For Soil Organic Matter(SOM)prediction,it achieves(R^(2)=0.79%,RMSE=1.42%)and(R^(2)=0.76%,RMSE=1.3%),respectively.The data fusion has improved the soil property prediction.The highest improvement was obtained for the SOM property(R^(2)=0.80%,RMSE=1.39)when using the SVR model and applying the second Combination C2(20% of IS and 80%LS).展开更多
Assimilating Sentinel-2 images with the CERES-Wheat model can improve the precision of winter wheat yield estimates at a regional scale. To verify this method, we applied the ensemble Kalman filter(EnKF) to assimilate...Assimilating Sentinel-2 images with the CERES-Wheat model can improve the precision of winter wheat yield estimates at a regional scale. To verify this method, we applied the ensemble Kalman filter(EnKF) to assimilate the leaf area index(LAI) derived from Sentinel-2 data and simulated by the CERES-Wheat model. From this, we obtained the assimilated daily LAI during the growth stage of winter wheat across three counties located in the southeast of the Loess Plateau in China: Xiangfen, Xinjiang, and Wenxi. We assigned LAI weights at different growth stages by comparing the improved analytic hierarchy method, the entropy method, and the normalized combination weighting method, and constructed a yield estimation model with the measurements to accurately estimate the yield of winter wheat. We found that the changes of assimilated LAI during the growth stage of winter wheat strongly agreed with the simulated LAI. With the correction of the derived LAI from the Sentinel-2 images, the LAI from the green-up stage to the heading–filling stage was enhanced, while the LAI decrease from the milking stage was slowed down, which was more in line with the actual changes of LAI for winter wheat. We also compared the simulated and derived LAI and found the assimilated LAI had reduced the root mean square error(RMSE) by 0.43 and 0.29 m^(2) m^(–2), respectively, based on the measured LAI. The assimilation improved the estimation accuracy of the LAI time series. The highest determination coefficient(R2) was 0.8627 and the lowest RMSE was 472.92 kg ha^(–1) in the regression of the yields estimated by the normalized weighted assimilated LAI method and measurements. The relative error of the estimated yield of winter wheat in the study counties was less than 1%, suggesting that Sentinel-2 data with high spatial-temporal resolution can be assimilated with the CERES-Wheat model to obtain more accurate regional yield estimates.展开更多
BACKGROUND Combined hepatocellular-cholangiocarcinoma(CHC)is a rare type of primary liver cancer.Due to its complex histopathological characteristics,the imaging features of CHC can overlap with those of hepatocellula...BACKGROUND Combined hepatocellular-cholangiocarcinoma(CHC)is a rare type of primary liver cancer.Due to its complex histopathological characteristics,the imaging features of CHC can overlap with those of hepatocellular carcinoma(HCC)and intrahepatic cholangiocarcinoma(ICC).AIM To investigate the possibility and efficacy of differentiating CHC from HCC and ICC by using contrast-enhanced ultrasound(CEUS)Liver Imaging Reporting and Data System(LI-RADS)and tumor biomarkers.METHODS Between January 2016 and December 2019,patients with histologically confirmed CHC,ICC and HCC with chronic liver disease were enrolled.The diagnostic formula for CHC was as follows:(1)LR-5 or LR-M with elevated alphafetoprotein(AFP)and carbohydrate antigen 19-9(CA19-9);(2)LR-M with elevated AFP and normal CA19-9;or(3)LR-5 with elevated CA19-9 and normal AFP.The sensitivity,specificity,accuracy and area under the receiver operating characteristic curve were calculated to determine the diagnostic value of the criteria.RESULTS After propensity score matching,134 patients(mean age of 51.4±9.4 years,108 men)were enrolled,including 35 CHC,29 ICC and 70 HCC patients.Based on CEUS LI-RADS classification,74.3%(26/35)and 25.7%(9/35)of CHC lesions were assessed as LR-M and LR-5,respectively.The rates of elevated AFP and CA19-9 in CHC patients were 51.4%and 11.4%,respectively,and simultaneous elevations of AFP and CA19-9 were found in 8.6%(3/35)of CHC patients.The sensitivity,specificity,positive predictive value,negative predictive value,accuracy and area under the receiver operating characteristic curve of the aforementioned diagnostic criteria for discriminating CHC from HCC and ICC were 40.0%,89.9%,58.3%,80.9%,76.9%and 0.649,respectively.When considering the reported prevalence of CHC(0.4%-14.2%),the positive predictive value and NPV were revised to 1.6%-39.6%and 90.1%-99.7%,respectively.CONCLUSION CHCs are more likely to be classified as LR-M than LR-5 by CEUS LI-RADS.The combination of the CEUS LI-RADS classification with serum tumor markers shows high specificity but low sensitivity for the diagnosis of CHC.Moreover,CHC could be confidently excluded with high NPV.展开更多
Multi-sensor and multi-resolution source images consisting of optical and long-wave infrared (LWlR) images are analyzed separately and then combined for urban mapping in this study.The framework of its methodology is ...Multi-sensor and multi-resolution source images consisting of optical and long-wave infrared (LWlR) images are analyzed separately and then combined for urban mapping in this study.The framework of its methodology is based on a two-level classification approach.In the first level,contributions of these two data sources in urban mapping are examined extensively by four types of classifications,i.e.spectral-based,spectral-spatial-based,joint classification,and multiple feature classification.In the second level,an objected-based approach is applied to decline the boundaries.The specificity of our proposed framework not only lies in the combination of two different images,but also the exploration of the LWlR image as one complementary spectral information for urban mapping.To verify the effectiveness of the presented classification framework and to confirm the LWlR's complementary role in the urban mapping task,experiment results are evaluated by the grss_dfc_2014 data-set.展开更多
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R196),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Pedo-spectroscopy has the potential to provide valuable information about soil physical,chemical,and biological properties.Nowadays,wemay predict soil properties usingVNIRfield imaging spectra(IS)such as Prisma satellite data or laboratory spectra(LS).The primary goal of this study is to investigate machine learning models namely Partial Least Squares Regression(PLSR)and Support Vector Regression(SVR)for the prediction of several soil properties,including clay,sand,silt,organic matter,nitrate NO3-,and calcium carbonate CaCO_(3),using five VNIR spectra dataset combinations(%IS,%LS)as follows:C1(0%IS,100%LS),C2(20%IS,80%LS),C3(50%IS,50%LS),C4(80%IS,20%LS)and C5(100%IS,0%LS).Soil samples were collected at bare soils and at the upper(0–30 cm)layer.The data set has been split into a training dataset 80%of the collected data(n=248)and a validation dataset 20%of the collected data(n=61).The proposed PLSR and SVR models were trained then tested for each dataset combination.According to our results,SVR outperforms PLSR for both:C1(0%IS,100%LS)and C5(100%IS,0%LS).For Soil Organic Matter(SOM)prediction,it achieves(R^(2)=0.79%,RMSE=1.42%)and(R^(2)=0.76%,RMSE=1.3%),respectively.The data fusion has improved the soil property prediction.The highest improvement was obtained for the SOM property(R^(2)=0.80%,RMSE=1.39)when using the SVR model and applying the second Combination C2(20% of IS and 80%LS).
基金supported by the National Key Research and Development Program of China (2018YFD020040103)the National Key Research and Development Program of Shanxi Province, China (201803D221005-2)。
文摘Assimilating Sentinel-2 images with the CERES-Wheat model can improve the precision of winter wheat yield estimates at a regional scale. To verify this method, we applied the ensemble Kalman filter(EnKF) to assimilate the leaf area index(LAI) derived from Sentinel-2 data and simulated by the CERES-Wheat model. From this, we obtained the assimilated daily LAI during the growth stage of winter wheat across three counties located in the southeast of the Loess Plateau in China: Xiangfen, Xinjiang, and Wenxi. We assigned LAI weights at different growth stages by comparing the improved analytic hierarchy method, the entropy method, and the normalized combination weighting method, and constructed a yield estimation model with the measurements to accurately estimate the yield of winter wheat. We found that the changes of assimilated LAI during the growth stage of winter wheat strongly agreed with the simulated LAI. With the correction of the derived LAI from the Sentinel-2 images, the LAI from the green-up stage to the heading–filling stage was enhanced, while the LAI decrease from the milking stage was slowed down, which was more in line with the actual changes of LAI for winter wheat. We also compared the simulated and derived LAI and found the assimilated LAI had reduced the root mean square error(RMSE) by 0.43 and 0.29 m^(2) m^(–2), respectively, based on the measured LAI. The assimilation improved the estimation accuracy of the LAI time series. The highest determination coefficient(R2) was 0.8627 and the lowest RMSE was 472.92 kg ha^(–1) in the regression of the yields estimated by the normalized weighted assimilated LAI method and measurements. The relative error of the estimated yield of winter wheat in the study counties was less than 1%, suggesting that Sentinel-2 data with high spatial-temporal resolution can be assimilated with the CERES-Wheat model to obtain more accurate regional yield estimates.
基金National Natural Science Foundation of China,No.81571697The Science and Technology Department of Sichuan Province,No.2017SZ0003 and No.2018FZ0044.
文摘BACKGROUND Combined hepatocellular-cholangiocarcinoma(CHC)is a rare type of primary liver cancer.Due to its complex histopathological characteristics,the imaging features of CHC can overlap with those of hepatocellular carcinoma(HCC)and intrahepatic cholangiocarcinoma(ICC).AIM To investigate the possibility and efficacy of differentiating CHC from HCC and ICC by using contrast-enhanced ultrasound(CEUS)Liver Imaging Reporting and Data System(LI-RADS)and tumor biomarkers.METHODS Between January 2016 and December 2019,patients with histologically confirmed CHC,ICC and HCC with chronic liver disease were enrolled.The diagnostic formula for CHC was as follows:(1)LR-5 or LR-M with elevated alphafetoprotein(AFP)and carbohydrate antigen 19-9(CA19-9);(2)LR-M with elevated AFP and normal CA19-9;or(3)LR-5 with elevated CA19-9 and normal AFP.The sensitivity,specificity,accuracy and area under the receiver operating characteristic curve were calculated to determine the diagnostic value of the criteria.RESULTS After propensity score matching,134 patients(mean age of 51.4±9.4 years,108 men)were enrolled,including 35 CHC,29 ICC and 70 HCC patients.Based on CEUS LI-RADS classification,74.3%(26/35)and 25.7%(9/35)of CHC lesions were assessed as LR-M and LR-5,respectively.The rates of elevated AFP and CA19-9 in CHC patients were 51.4%and 11.4%,respectively,and simultaneous elevations of AFP and CA19-9 were found in 8.6%(3/35)of CHC patients.The sensitivity,specificity,positive predictive value,negative predictive value,accuracy and area under the receiver operating characteristic curve of the aforementioned diagnostic criteria for discriminating CHC from HCC and ICC were 40.0%,89.9%,58.3%,80.9%,76.9%and 0.649,respectively.When considering the reported prevalence of CHC(0.4%-14.2%),the positive predictive value and NPV were revised to 1.6%-39.6%and 90.1%-99.7%,respectively.CONCLUSION CHCs are more likely to be classified as LR-M than LR-5 by CEUS LI-RADS.The combination of the CEUS LI-RADS classification with serum tumor markers shows high specificity but low sensitivity for the diagnosis of CHC.Moreover,CHC could be confidently excluded with high NPV.
文摘Multi-sensor and multi-resolution source images consisting of optical and long-wave infrared (LWlR) images are analyzed separately and then combined for urban mapping in this study.The framework of its methodology is based on a two-level classification approach.In the first level,contributions of these two data sources in urban mapping are examined extensively by four types of classifications,i.e.spectral-based,spectral-spatial-based,joint classification,and multiple feature classification.In the second level,an objected-based approach is applied to decline the boundaries.The specificity of our proposed framework not only lies in the combination of two different images,but also the exploration of the LWlR image as one complementary spectral information for urban mapping.To verify the effectiveness of the presented classification framework and to confirm the LWlR's complementary role in the urban mapping task,experiment results are evaluated by the grss_dfc_2014 data-set.
基金亚太森林恢复与可持续管理网络项目"Forest Cover and Aboveground Biomass Mapping in the Greater Mekong Subregion and Malaysia"(编号:2011PA004)国家863课题"全球森林生物量和碳储量遥感估测关键技术(编号:2012AA12A306)"资助
文摘目的比较3.0 T MRI 3种扫描序列对颈神经根成像的应用价值。材料与方法收集临床需要扫描颈椎MRI患者37例,均行常规序列、三维双回波稳态(three-dimensional double-echo steady state,3D-DESS)序列、多回波数据图像重合(multi-echo data image combination,MEDIC)序列、可变反转角三维快速自旋回波(3D sampling perfection with application optimized contrasts using different flip angle evolutions,3D-SPACE)序列扫描,所有图像均进行后处理重建,对图像质量和正常颈神经根显示、受压颈神经显示清晰度、颈神经根与邻近组织的对比噪声比(contrast noise ratio,CNR)3个方面进行评价。结果 3种扫描序列3个方面两两比较,3D-DESS序列神经根-椎体CNR和神经根-脑脊液CNR均高于MEDIC序列;3D-DESS序列神经根-椎体CNR高于3D-SPACE序列,而3D-DESS序列神经根-脑脊液CNR低于3D-SPACE序列;3D-SPACE序列神经根-脑脊液CNR高于MEDIC序列,3D-SPACE序列神经根-椎体CNR与MEDIC序列差异无统计学意义。图像质量评分3种序列两两对比差异均有统计学意义(P<0.05),3D-DESS序列优于MEDIC序列,而3D-SPACE序列图像质量最差。对受压神经根清晰度的显示,3D-DESS序列和MEDIC序列差异无统计学意义,两序列与3D-SPACE序列对比,差异均有统计学意义(P<0.05),即两序列均优于3D-SPACE序列。结论对于显示神经根结构和诊断神经根病变等方面,3D-DESS序列明显优于MEDIC序列和3D-SPACE序列,对颈神经根成像及颈神经根受压的临床诊断更具优势。
文摘为实现地貌类型快速划分,以前人研究成果为基础,采用DEM(Digital Elevation Modeldata)数据,将地貌信息提取流程化:利用均值变点分析法获取宏观地形因子的最佳窗口、根据算法模型提取可反映地貌信息的8种地形因子、对各地形因子统一量纲、获得地形因子间相关系数矩阵、采用雪式熵值法取得最佳地形因子组合,最后通过ENVI(The Environment for Visualizing Images)软件中的非监督分类实现地貌类型划分。基于ENVI二次开发平台,采用IDL(Interactive Data Language)语言进行编程实现,对地貌类型实现全(半)自动智能、快速划分。并以长白山作为研究区,从宏观和微观对地貌进行划分,并对分类结果评价分析,结果较好。该系统的实现,不仅对地貌类型提取流程化、集成化,对我国广大范围内地貌填图也具有重要的现实意义。
