Biochemical components of Moso bamboo(Phyllostachys pubescens)are critical to physiological and ecological processes and play an important role in the material and energy cycles of the ecosystem.The coupled PROSPECT w...Biochemical components of Moso bamboo(Phyllostachys pubescens)are critical to physiological and ecological processes and play an important role in the material and energy cycles of the ecosystem.The coupled PROSPECT with SAIL(PROSAIL)radiative transfer model is widely used for vegetation biochemical component content inversion.However,the presence of leaf-eating pests,such as Pantana phyllostachysae Chao(PPC),weakens the performance of the model for estimating biochemical components of Moso bamboo and thus must be considered.Therefore,this study considered pest-induced stress signals associated with Sentinel-2A/B images and field data and established multiple sets of biochemical canopy reflectance look-up tables(LUTs)based on the PROSAIL framework by setting different parameter ranges according to infestation levels.Quantitative inversions of leaf area index(LAI),leaf chlorophyll content(LCC),and leaf equivalent water thickness(LEWT)were derived.The scale conversions from LCC to canopy chlorophyll content(CCC)and LEWT to canopy equivalent water thickness(CEWT)were calculated.The results showed that LAI,CCC,and CEWT were inversely related with PPC-induced stress.When applying multiple LUTs,the p-values were<0.01;the R2 values for LAI,CCC,and CEWT were 0.71,0.68,and 0.65 with root mean square error(RMSE)(normalized RMSE,NRMSE)values of 0.38(0.16),17.56μg cm-2(0.20),and 0.02 cm(0.51),respectively.Compared to the values obtained for the traditional PROSAIL model,for October,R2 values increased by 0.05 and 0.10 and NRMSE decreased by 0.09 and 0.02 for CCC and CEWT,respectively and RMSE decreased by 0.35μg cm-2 for CCC.The feasibility of the inverse strategy for integrating pest-induced stress factors into the PROSAIL model,while establishing multiple LUTs under different pest-induced damage levels,was successfully demonstrated and can potentially enhance future vegetation parameter inversion and monitoring of bamboo forest health and ecosystems.展开更多
Rapid and accurate acquisition of soil organic matter(SOM)information in cultivated land is important for sustainable agricultural development and carbon balance management.This study proposed a novel approach to pred...Rapid and accurate acquisition of soil organic matter(SOM)information in cultivated land is important for sustainable agricultural development and carbon balance management.This study proposed a novel approach to predict SOM with high accuracy using multiyear synthetic remote sensing variables on a monthly scale.We obtained 12 monthly synthetic Sentinel-2 images covering the study area from 2016 to 2021 through the Google Earth Engine(GEE)platform,and reflectance bands and vegetation indices were extracted from these composite images.Then the random forest(RF),support vector machine(SVM)and gradient boosting regression tree(GBRT)models were tested to investigate the difference in SOM prediction accuracy under different combinations of monthly synthetic variables.Results showed that firstly,all monthly synthetic spectral bands of Sentinel-2 showed a significant correlation with SOM(P<0.05)for the months of January,March,April,October,and November.Secondly,in terms of single-monthly composite variables,the prediction accuracy was relatively poor,with the highest R^(2)value of 0.36 being observed in January.When monthly synthetic environmental variables were grouped in accordance with the four quarters of the year,the first quarter and the fourth quarter showed good performance,and any combination of three quarters was similar in estimation accuracy.The overall best performance was observed when all monthly synthetic variables were incorporated into the models.Thirdly,among the three models compared,the RF model was consistently more accurate than the SVM and GBRT models,achieving an R^(2)value of 0.56.Except for band 12 in December,the importance of the remaining bands did not exhibit significant differences.This research offers a new attempt to map SOM with high accuracy and fine spatial resolution based on monthly synthetic Sentinel-2 images.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
Existing eddy current non-destructive testing(NDT) techniques generally do not consider the inclination angle of inclined cracks, which potentially harms a larger region of a tested structure. This work proposes the u...Existing eddy current non-destructive testing(NDT) techniques generally do not consider the inclination angle of inclined cracks, which potentially harms a larger region of a tested structure. This work proposes the use of 2 D scan images generated by using pulsed eddy current(PEC) non-destructive testing(NDT) technique in the quantification of the inclination and depth of inclined cracks. The image-based feature extraction technique e ectively identifies the crack axis, which consequently enables extraction of features from the extracted linear scans. The technique extracts linear scans from the images to allow the extraction of three novel image-based features, namely the length of extracted linear scans(LLS), the linear scan skewness(LSS), and the highest value on linear scan(LSmax). The correlation of the three features to surface crack inclination angles and depths were analysed and found to be highly dependent on the crack depths, while only LLS and LSS are correlated to the crack inclination angles.展开更多
Accurate landslide extraction is significant for landslide disaster prevention and control.Remote sensing images have been widely used in landslide investigation,and landslide extraction methods based on deep learning...Accurate landslide extraction is significant for landslide disaster prevention and control.Remote sensing images have been widely used in landslide investigation,and landslide extraction methods based on deep learning combined with remote sensing images(such as U-Net)have received a lot of attention.However,because of the variable shape and texture features of landslides in remote sensing images,the rich spectral features,and the complexity of their surrounding features,landslide extraction using U-Net can lead to problems such as false detection and missed detection.Therefore,this study introduces the channel attention mechanism called the squeeze-and-excitation network(SENet)in the feature fusion part of U-Net;the study also constructs an attention U-Net landside extraction model combining SENet and U-Net,and uses Sentinel-2A remote sensing images for model training and validation.The extraction results are evaluated through different evaluation metrics and compared with those of two models:U-Net and U-Net Backbone(U-Net Without Skip Connection).The results show that proposed the model can effectively extract landslides based on Sentinel-2A remote sensing images with an F1 value of 87.94%,which is about 2%and 3%higher than U-Net and U-Net Backbone,respectively,with less false detection and more accurate extraction results.展开更多
AIM:To review the literature on the assessment of venous vessels to estimate the penumbra on T2*w imaging and susceptibility-weighted imaging (SWI). METHODS:Literature that reported on the assessment of penumbra by T2...AIM:To review the literature on the assessment of venous vessels to estimate the penumbra on T2*w imaging and susceptibility-weighted imaging (SWI). METHODS:Literature that reported on the assessment of penumbra by T2*w imaging or SWI and used a validation method was included. PubMed and relevant stroke and magnetic resonance imaging (MRI) related conference abstracts were searched. Abstracts that had overlapping content with full text articles were excluded. The retrieved literature was scanned for further relevant references. Only clinical literature published in English was considered, patients with Moya-Moya syndrome were disregarded. Data is given as cumulative absolute and relative values, ranges are given where appropriate. RESULTS:Forty-three publications including 1145 patients could be identified. T2*w imaging was used in 16 publications (627 patients), SWI in 26 publications (453 patients). Only one publication used both (65 patients). The cumulative presence of hypointense vessel sign was 54% (range 32%-100%) for T2* (668 patients) and 81% (range 34%-100%) for SWI (334 patients). There was rare mentioning of interrater agreement (6 publications, 210 patients) and reliability (1 publication, 20 patients) but the numbers reported ranged from good to excellent. In most publications (n = 22) perfusion MRI was used as a validation method (617 patients). More patients were scanned in the subacute than in the acute phase (596 patients vs 320 patients). Clinical outcome was reported in 13 publications (521 patients) but was not consistent. CONCLUSION:The low presence of vessels signs on T2*w imaging makes SWI much more promising. More research is needed to obtain formal validation and quantification.展开更多
Mapping soil organic matter(SOM)content has become an important application of digital soil mapping.In this study,we processed all Sentinel-2 images covering the bare-soil period(March to June)in Northeast China from ...Mapping soil organic matter(SOM)content has become an important application of digital soil mapping.In this study,we processed all Sentinel-2 images covering the bare-soil period(March to June)in Northeast China from 2019 to 2022 and integrated the observation results into synthetic materials with four defined time intervals(10,15,20,and 30 d).Then,we used synthetic images corresponding to different time periods to conduct SOM mapping and determine the optimal time interval and time period beforefinally assessing the impacts of adding environmental covariates.The results showed the following:(1)in SOM mapping,the highest accuracy was obtained using day-of-year(DOY)120 to 140 synthetic images with 20 d time intervals,as well as with different time intervals,ranked as follows:20 d>30 d>15 d>10 d;(2)when using synthetic images at different time intervals to predict SOM,the best time period for predicting SOM was always within May;and(3)adding environmental covariates effectively improved the SOM mapping performance,and the multiyear average temperature was the most important factor.In general,our results demonstrated the valuable potential of SOM mapping using multiyear synthetic imagery,thereby allowing detailed mapping of large areas of cultivated soil.展开更多
为深入研究光学遥感图像中的船舶检测问题,提升检测精度和降低模型复杂度,设计基于改进旋转区域卷积和神经网络(Rotational Region Convolutional Neural Networks),R^(2)CNN的两阶段旋转框检测模型。在模型的第一阶段使用水平框作为候...为深入研究光学遥感图像中的船舶检测问题,提升检测精度和降低模型复杂度,设计基于改进旋转区域卷积和神经网络(Rotational Region Convolutional Neural Networks),R^(2)CNN的两阶段旋转框检测模型。在模型的第一阶段使用水平框作为候选区域;在模型第二阶段引入水平框预测分支,并且设计一种间接预测角度的回归模型;在测试阶段进行旋转框非极大值抑制时,设计基于掩码矩阵的旋转框IoU(Intersection over Union)算法。试验结果显示:改进R^(2)CNN模型在HRSC2016(High Resolution Ship Collection 2016)数据集上取得81.0%的平均精确度,相比其他模型均有不同程度的提升,说明改进R^(2)CNN在简化模型的同时能有效提升使用旋转框检测船舶的性能。展开更多
Landslide detection is a hot topic in the remote sensing community,particularly with the current rapid growth in volume(and variety)of Earth observation data and the substantial progress of computer vision.Deep learni...Landslide detection is a hot topic in the remote sensing community,particularly with the current rapid growth in volume(and variety)of Earth observation data and the substantial progress of computer vision.Deep learning algorithms,especially fully convolutional networks(FCNs),and variations like the ResU-Net have been used recently as rapid and automatic landslide detection approaches.Although FCNs have shown cutting-edge results in automatic landslide detection,accuracy can be improved by adding prior knowledge through possible frameworks.This study evaluates a rulebased object-based image analysis(OBIA)approach built on probabilities resulting from the ResU-Net model for landslide detection.We train the ResU-Net model using a landslide dataset comprising landslide inventories from various geographic regions,including our study area and test the testing area not used for training.In the OBIA stage,we frst calculate land cover and image difference indices for pre-and post-landslide multi-temporal images.Next,we use the generated indices and the resulting ResU-Net probabilities for image segmentation;the extracted landslide object candidates are then optimized using rule-based classification.In the result validation section,the landslide detection of the proposed integration of the ResU-Net with a rule-based classification of OBIA is compared with that of the ResU-Net alone.Our proposed approach improves the mean intersection-over-union of the resulting map from the ResU-Net by more than 22%.展开更多
基金funded by the National Natural Science Foundation of China(42071300)the Fujian Province Natural Science(2020J01504)+4 种基金the China Postdoctoral Science Foundation(2018M630728)the Open Fund of Fujian Provincial Key Laboratory of Resources and Environment Monitoring&Sustainable Management and Utilization(ZD202102)the Program for Innovative Research Team in Science and Technology in Fujian Province University(KC190002)the Open Fund of University Key Lab of Geomatics Technology and Optimize Resources Utilization in Fujian Province(fafugeo201901)supported by the Research Project of Jinjiang Fuda Science and Education Park Development Center(2019-JJFDKY-17)。
文摘Biochemical components of Moso bamboo(Phyllostachys pubescens)are critical to physiological and ecological processes and play an important role in the material and energy cycles of the ecosystem.The coupled PROSPECT with SAIL(PROSAIL)radiative transfer model is widely used for vegetation biochemical component content inversion.However,the presence of leaf-eating pests,such as Pantana phyllostachysae Chao(PPC),weakens the performance of the model for estimating biochemical components of Moso bamboo and thus must be considered.Therefore,this study considered pest-induced stress signals associated with Sentinel-2A/B images and field data and established multiple sets of biochemical canopy reflectance look-up tables(LUTs)based on the PROSAIL framework by setting different parameter ranges according to infestation levels.Quantitative inversions of leaf area index(LAI),leaf chlorophyll content(LCC),and leaf equivalent water thickness(LEWT)were derived.The scale conversions from LCC to canopy chlorophyll content(CCC)and LEWT to canopy equivalent water thickness(CEWT)were calculated.The results showed that LAI,CCC,and CEWT were inversely related with PPC-induced stress.When applying multiple LUTs,the p-values were<0.01;the R2 values for LAI,CCC,and CEWT were 0.71,0.68,and 0.65 with root mean square error(RMSE)(normalized RMSE,NRMSE)values of 0.38(0.16),17.56μg cm-2(0.20),and 0.02 cm(0.51),respectively.Compared to the values obtained for the traditional PROSAIL model,for October,R2 values increased by 0.05 and 0.10 and NRMSE decreased by 0.09 and 0.02 for CCC and CEWT,respectively and RMSE decreased by 0.35μg cm-2 for CCC.The feasibility of the inverse strategy for integrating pest-induced stress factors into the PROSAIL model,while establishing multiple LUTs under different pest-induced damage levels,was successfully demonstrated and can potentially enhance future vegetation parameter inversion and monitoring of bamboo forest health and ecosystems.
基金National Key Research and Development Program of China(2022YFB3903302 and 2021YFC1809104)。
文摘Rapid and accurate acquisition of soil organic matter(SOM)information in cultivated land is important for sustainable agricultural development and carbon balance management.This study proposed a novel approach to predict SOM with high accuracy using multiyear synthetic remote sensing variables on a monthly scale.We obtained 12 monthly synthetic Sentinel-2 images covering the study area from 2016 to 2021 through the Google Earth Engine(GEE)platform,and reflectance bands and vegetation indices were extracted from these composite images.Then the random forest(RF),support vector machine(SVM)and gradient boosting regression tree(GBRT)models were tested to investigate the difference in SOM prediction accuracy under different combinations of monthly synthetic variables.Results showed that firstly,all monthly synthetic spectral bands of Sentinel-2 showed a significant correlation with SOM(P<0.05)for the months of January,March,April,October,and November.Secondly,in terms of single-monthly composite variables,the prediction accuracy was relatively poor,with the highest R^(2)value of 0.36 being observed in January.When monthly synthetic environmental variables were grouped in accordance with the four quarters of the year,the first quarter and the fourth quarter showed good performance,and any combination of three quarters was similar in estimation accuracy.The overall best performance was observed when all monthly synthetic variables were incorporated into the models.Thirdly,among the three models compared,the RF model was consistently more accurate than the SVM and GBRT models,achieving an R^(2)value of 0.56.Except for band 12 in December,the importance of the remaining bands did not exhibit significant differences.This research offers a new attempt to map SOM with high accuracy and fine spatial resolution based on monthly synthetic Sentinel-2 images.
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Supported by Malaysia’s Ministry of Higher Education(Grant No.FRGS16-059-0558)
文摘Existing eddy current non-destructive testing(NDT) techniques generally do not consider the inclination angle of inclined cracks, which potentially harms a larger region of a tested structure. This work proposes the use of 2 D scan images generated by using pulsed eddy current(PEC) non-destructive testing(NDT) technique in the quantification of the inclination and depth of inclined cracks. The image-based feature extraction technique e ectively identifies the crack axis, which consequently enables extraction of features from the extracted linear scans. The technique extracts linear scans from the images to allow the extraction of three novel image-based features, namely the length of extracted linear scans(LLS), the linear scan skewness(LSS), and the highest value on linear scan(LSmax). The correlation of the three features to surface crack inclination angles and depths were analysed and found to be highly dependent on the crack depths, while only LLS and LSS are correlated to the crack inclination angles.
基金supported by the Project Supported by the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural Resources[grant number KF-2021-06-014]the National Natural Scientific Foundation of China[grant number 42201459]+2 种基金the Central Government to Guide Local Scientific and Technological Development[grant number 22ZY1QA005]Tianyou Youth Talent Lift Program of Lanzhou Jiaotong University,Young Doctoral Fund Project of Higher Education Institutions in Gansu Province[grant number 2022QB-058]State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR,CASM(2022-03-03).
文摘Accurate landslide extraction is significant for landslide disaster prevention and control.Remote sensing images have been widely used in landslide investigation,and landslide extraction methods based on deep learning combined with remote sensing images(such as U-Net)have received a lot of attention.However,because of the variable shape and texture features of landslides in remote sensing images,the rich spectral features,and the complexity of their surrounding features,landslide extraction using U-Net can lead to problems such as false detection and missed detection.Therefore,this study introduces the channel attention mechanism called the squeeze-and-excitation network(SENet)in the feature fusion part of U-Net;the study also constructs an attention U-Net landside extraction model combining SENet and U-Net,and uses Sentinel-2A remote sensing images for model training and validation.The extraction results are evaluated through different evaluation metrics and compared with those of two models:U-Net and U-Net Backbone(U-Net Without Skip Connection).The results show that proposed the model can effectively extract landslides based on Sentinel-2A remote sensing images with an F1 value of 87.94%,which is about 2%and 3%higher than U-Net and U-Net Backbone,respectively,with less false detection and more accurate extraction results.
文摘AIM:To review the literature on the assessment of venous vessels to estimate the penumbra on T2*w imaging and susceptibility-weighted imaging (SWI). METHODS:Literature that reported on the assessment of penumbra by T2*w imaging or SWI and used a validation method was included. PubMed and relevant stroke and magnetic resonance imaging (MRI) related conference abstracts were searched. Abstracts that had overlapping content with full text articles were excluded. The retrieved literature was scanned for further relevant references. Only clinical literature published in English was considered, patients with Moya-Moya syndrome were disregarded. Data is given as cumulative absolute and relative values, ranges are given where appropriate. RESULTS:Forty-three publications including 1145 patients could be identified. T2*w imaging was used in 16 publications (627 patients), SWI in 26 publications (453 patients). Only one publication used both (65 patients). The cumulative presence of hypointense vessel sign was 54% (range 32%-100%) for T2* (668 patients) and 81% (range 34%-100%) for SWI (334 patients). There was rare mentioning of interrater agreement (6 publications, 210 patients) and reliability (1 publication, 20 patients) but the numbers reported ranged from good to excellent. In most publications (n = 22) perfusion MRI was used as a validation method (617 patients). More patients were scanned in the subacute than in the acute phase (596 patients vs 320 patients). Clinical outcome was reported in 13 publications (521 patients) but was not consistent. CONCLUSION:The low presence of vessels signs on T2*w imaging makes SWI much more promising. More research is needed to obtain formal validation and quantification.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA28100000)the K.C.Wong Education Foundation,Jilin Provincial Development and Reform Commission Innovation Capacity Building Project(grant number 2021C044-10)the Special fund project for high-tech indus-trialization of science and technology cooperation between Jilin Province and the Chinese Academy of Sciences(2021SYHZ0013).
文摘Mapping soil organic matter(SOM)content has become an important application of digital soil mapping.In this study,we processed all Sentinel-2 images covering the bare-soil period(March to June)in Northeast China from 2019 to 2022 and integrated the observation results into synthetic materials with four defined time intervals(10,15,20,and 30 d).Then,we used synthetic images corresponding to different time periods to conduct SOM mapping and determine the optimal time interval and time period beforefinally assessing the impacts of adding environmental covariates.The results showed the following:(1)in SOM mapping,the highest accuracy was obtained using day-of-year(DOY)120 to 140 synthetic images with 20 d time intervals,as well as with different time intervals,ranked as follows:20 d>30 d>15 d>10 d;(2)when using synthetic images at different time intervals to predict SOM,the best time period for predicting SOM was always within May;and(3)adding environmental covariates effectively improved the SOM mapping performance,and the multiyear average temperature was the most important factor.In general,our results demonstrated the valuable potential of SOM mapping using multiyear synthetic imagery,thereby allowing detailed mapping of large areas of cultivated soil.
基金funded by the Institute of Advanced Research in Artificial Intelligence(IARAl)GmbHInstitute of Advanced Research in Artificial Intelligence(IARAl)GmbH Address:LandstraBer HauptstraBe 5,1030 Vienna,Austria[VAT number(UID):ATU74131236].
文摘Landslide detection is a hot topic in the remote sensing community,particularly with the current rapid growth in volume(and variety)of Earth observation data and the substantial progress of computer vision.Deep learning algorithms,especially fully convolutional networks(FCNs),and variations like the ResU-Net have been used recently as rapid and automatic landslide detection approaches.Although FCNs have shown cutting-edge results in automatic landslide detection,accuracy can be improved by adding prior knowledge through possible frameworks.This study evaluates a rulebased object-based image analysis(OBIA)approach built on probabilities resulting from the ResU-Net model for landslide detection.We train the ResU-Net model using a landslide dataset comprising landslide inventories from various geographic regions,including our study area and test the testing area not used for training.In the OBIA stage,we frst calculate land cover and image difference indices for pre-and post-landslide multi-temporal images.Next,we use the generated indices and the resulting ResU-Net probabilities for image segmentation;the extracted landslide object candidates are then optimized using rule-based classification.In the result validation section,the landslide detection of the proposed integration of the ResU-Net with a rule-based classification of OBIA is compared with that of the ResU-Net alone.Our proposed approach improves the mean intersection-over-union of the resulting map from the ResU-Net by more than 22%.
文摘为提高灌溉农田中灌溉水体的识别精度,以河套灌区解放闸灌域作为研究区,基于Sentinel-2遥感影像,结合灌区实际情况对地表水体提取模型(WatNet)进行改进,得到MWatNet模型并提取灌溉水体。采用总体精度(Overall accuracy,OA)、平均交并比(Mean intersection over union,MIoU)、F1值等水体提取精度指标进行综合评价。结果表明:改进后的地表水体提取模型(MWatNet)在解放闸灌域农田灌溉水体的提取上具有较好的识别精度,模型总体精度达到96%,平均交并比达到83%,F1值为80%,实地调研验证准确度为85.7%;对比原WatNet、水体语义分割模型(Deeplabv3_plus)和水体提取模型(Deepwatermapv2),MWatNet在灌溉水体提取的连结性、剔除道路和城镇干扰等方面,均表现出更好的效果和模型运行效率。利用该模型可以实现灌溉水体定量化表征,为灌溉用水调度提供了数据支撑。
