Because land cover plays an important role in global climate change studies, assessing the agreement among different land cover products is critical. Significant discrepancies have been reported among satellite-derive...Because land cover plays an important role in global climate change studies, assessing the agreement among different land cover products is critical. Significant discrepancies have been reported among satellite-derived land cover products, especially at the regional scale. Dif- ferent classification schemes are a key obstacle to the comparison of products and are considered the main fac- tor behind the disagreement among the different products. Using a feature-based overlap metric, we investigated the degree of spatial agreement and quantified the overall and class-specific agreement among the Moderate Resolution Imaging Spectoradiometer （MODIS）, Global Land Cover 2000 （GLC2000）, and the National Land Cover/Use Data- sets （NLCD） products, and the author assessed the prod- ucts by ground reference data at the regional scale over China. The areas with a low degree of agreement mostly occurred in heterogeneous terrain and transition zones, while the areas with a high degree of agreement occurred in major plains and areas with homogeneous vegetation. The overall agreement of the MODIS and GLC2000 products was 50.8% and 52.9%, and the overall accuracy was 50.3% and 41.9%, respectively. Class-specific agree- ment or accuracy varied significantly. The high-agreement classes are water, grassland, cropland, snow and ice, and bare areas, whereas classes with low agreement are shru- bland and wetland in both MODIS and GLC2000. These characteristics of spatial patterns and quantitative agree- ment could be partly explained by the complex landscapes, mixed vegetation, low separability of spectro-temporal- texture signals, and coarse pixels. The differences of class definition among different the classification schemes also affects the agreement. Each product had its advantages and limitations, but neither the overall accuracy nor the class-specific accuracy could meet the requirements of climate modeling.展开更多
To compare finite element analysis(FEA)predictions and stereovision digital image correlation(StereoDIC)strain measurements at the same spatial positions throughout a region of interest,a field comparison procedure is...To compare finite element analysis(FEA)predictions and stereovision digital image correlation(StereoDIC)strain measurements at the same spatial positions throughout a region of interest,a field comparison procedure is developed.The procedure includes(a)conversion of the finite element data into a triangular mesh,(b)selection of a common coordinate system,(c)determination of the rigid body transformation to place both measurements and FEA data in the same system and(d)interpolation of the FEA nodal information to the same spatial locations as the StereoDIC measurements using barycentric coordinates.For an aluminum Al-6061 double edge notched tensile specimen,FEA results are obtained using both the von Mises isotropic yield criterion and Hill’s quadratic anisotropic yield criterion,with the unknown Hill model parameters determined using full-field specimen strain measurements for the nominally plane stress specimen.Using Hill’s quadratic anisotropic yield criterion,the point-by-point comparison of experimentally based full-field strains and stresses to finite element predictions are shown to be in excellent agreement,confirming the effectiveness of the field comparison process.展开更多
Seal authentication is an important task for verifying the authenticity of stamped seals used in various domains to protect legal documents from tampering and counterfeiting.Stamped seal inspection is commonly audited...Seal authentication is an important task for verifying the authenticity of stamped seals used in various domains to protect legal documents from tampering and counterfeiting.Stamped seal inspection is commonly audited manually to ensure document authenticity.However,manual assessment of seal images is tedious and laborintensive due to human errors,inconsistent placement,and completeness of the seal.Traditional image recognition systems are inadequate enough to identify seal types accurately,necessitating a neural network-based method for seal image recognition.However,neural network-based classification algorithms,such as Residual Networks(ResNet)andVisualGeometryGroup with 16 layers(VGG16)yield suboptimal recognition rates on stamp datasets.Additionally,the fixed training data categories make handling new categories to be a challenging task.This paper proposes amulti-stage seal recognition algorithmbased on Siamese network to overcome these limitations.Firstly,the seal image is pre-processed by applying an image rotation correction module based on Histogram of Oriented Gradients(HOG).Secondly,the similarity between input seal image pairs is measured by utilizing a similarity comparison module based on the Siamese network.Finally,we compare the results with the pre-stored standard seal template images in the database to obtain the seal type.To evaluate the performance of the proposed method,we further create a new seal image dataset that contains two subsets with 210,000 valid labeled pairs in total.The proposed work has a practical significance in industries where automatic seal authentication is essential as in legal,financial,and governmental sectors,where automatic seal recognition can enhance document security and streamline validation processes.Furthermore,the experimental results show that the proposed multi-stage method for seal image recognition outperforms state-of-the-art methods on the two established datasets.展开更多
A new methodology of comparing digital raster maps was proposed which allows not only detecting changes in the maps, but also obtaining quantitative measures of the importance of selected differences. Procedure of obj...A new methodology of comparing digital raster maps was proposed which allows not only detecting changes in the maps, but also obtaining quantitative measures of the importance of selected differences. Procedure of object interpretation of satellite images and forming of OMT (Object Map of Territory) is described. A list of allowable differences between two OMTs is defined. Two steps technique of quantitative measuring is proposed. At the first stage functions are constructed for calculating local measures of differences in the amount, areas and locations of objects on the map, as well as relations between the objects. In the second stage local measures are used to calculate the integral measure in order to get generalized assessment of difference between maps. The methods for constructing functions which calculate local and integral measures of differences are described. Examples of comparing and measuring the differences between OMTs are provided. Obtained results by utilizing this technique can be used to analyze trends, forecast of development and might be helpful for choosing most efficient scenarios for sustainable spatial planning and land management.展开更多
基金supported by the National Basic Research Program of China (Grant No. 2009CB723904)the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA05090201)the National Natural Science Foundation of China (Grant No. 40810059003)
文摘Because land cover plays an important role in global climate change studies, assessing the agreement among different land cover products is critical. Significant discrepancies have been reported among satellite-derived land cover products, especially at the regional scale. Dif- ferent classification schemes are a key obstacle to the comparison of products and are considered the main fac- tor behind the disagreement among the different products. Using a feature-based overlap metric, we investigated the degree of spatial agreement and quantified the overall and class-specific agreement among the Moderate Resolution Imaging Spectoradiometer （MODIS）, Global Land Cover 2000 （GLC2000）, and the National Land Cover/Use Data- sets （NLCD） products, and the author assessed the prod- ucts by ground reference data at the regional scale over China. The areas with a low degree of agreement mostly occurred in heterogeneous terrain and transition zones, while the areas with a high degree of agreement occurred in major plains and areas with homogeneous vegetation. The overall agreement of the MODIS and GLC2000 products was 50.8% and 52.9%, and the overall accuracy was 50.3% and 41.9%, respectively. Class-specific agree- ment or accuracy varied significantly. The high-agreement classes are water, grassland, cropland, snow and ice, and bare areas, whereas classes with low agreement are shru- bland and wetland in both MODIS and GLC2000. These characteristics of spatial patterns and quantitative agree- ment could be partly explained by the complex landscapes, mixed vegetation, low separability of spectro-temporal- texture signals, and coarse pixels. The differences of class definition among different the classification schemes also affects the agreement. Each product had its advantages and limitations, but neither the overall accuracy nor the class-specific accuracy could meet the requirements of climate modeling.
基金Financial support provided by Correlated Solutions Incorporated to perform StereoDIC experimentsthe Department of Mechanical Engineering at the University of South Carolina for simulation studies is deeply appreciated.
文摘To compare finite element analysis(FEA)predictions and stereovision digital image correlation(StereoDIC)strain measurements at the same spatial positions throughout a region of interest,a field comparison procedure is developed.The procedure includes(a)conversion of the finite element data into a triangular mesh,(b)selection of a common coordinate system,(c)determination of the rigid body transformation to place both measurements and FEA data in the same system and(d)interpolation of the FEA nodal information to the same spatial locations as the StereoDIC measurements using barycentric coordinates.For an aluminum Al-6061 double edge notched tensile specimen,FEA results are obtained using both the von Mises isotropic yield criterion and Hill’s quadratic anisotropic yield criterion,with the unknown Hill model parameters determined using full-field specimen strain measurements for the nominally plane stress specimen.Using Hill’s quadratic anisotropic yield criterion,the point-by-point comparison of experimentally based full-field strains and stresses to finite element predictions are shown to be in excellent agreement,confirming the effectiveness of the field comparison process.
基金the National Natural Science Foundation of China(Grant No.62172132)Public Welfare Technology Research Project of Zhejiang Province(Grant No.LGF21F020014)the Opening Project of Key Laboratory of Public Security Information Application Based on Big-Data Architecture,Ministry of Public Security of Zhejiang Police College(Grant No.2021DSJSYS002).
文摘Seal authentication is an important task for verifying the authenticity of stamped seals used in various domains to protect legal documents from tampering and counterfeiting.Stamped seal inspection is commonly audited manually to ensure document authenticity.However,manual assessment of seal images is tedious and laborintensive due to human errors,inconsistent placement,and completeness of the seal.Traditional image recognition systems are inadequate enough to identify seal types accurately,necessitating a neural network-based method for seal image recognition.However,neural network-based classification algorithms,such as Residual Networks(ResNet)andVisualGeometryGroup with 16 layers(VGG16)yield suboptimal recognition rates on stamp datasets.Additionally,the fixed training data categories make handling new categories to be a challenging task.This paper proposes amulti-stage seal recognition algorithmbased on Siamese network to overcome these limitations.Firstly,the seal image is pre-processed by applying an image rotation correction module based on Histogram of Oriented Gradients(HOG).Secondly,the similarity between input seal image pairs is measured by utilizing a similarity comparison module based on the Siamese network.Finally,we compare the results with the pre-stored standard seal template images in the database to obtain the seal type.To evaluate the performance of the proposed method,we further create a new seal image dataset that contains two subsets with 210,000 valid labeled pairs in total.The proposed work has a practical significance in industries where automatic seal authentication is essential as in legal,financial,and governmental sectors,where automatic seal recognition can enhance document security and streamline validation processes.Furthermore,the experimental results show that the proposed multi-stage method for seal image recognition outperforms state-of-the-art methods on the two established datasets.
文摘A new methodology of comparing digital raster maps was proposed which allows not only detecting changes in the maps, but also obtaining quantitative measures of the importance of selected differences. Procedure of object interpretation of satellite images and forming of OMT (Object Map of Territory) is described. A list of allowable differences between two OMTs is defined. Two steps technique of quantitative measuring is proposed. At the first stage functions are constructed for calculating local measures of differences in the amount, areas and locations of objects on the map, as well as relations between the objects. In the second stage local measures are used to calculate the integral measure in order to get generalized assessment of difference between maps. The methods for constructing functions which calculate local and integral measures of differences are described. Examples of comparing and measuring the differences between OMTs are provided. Obtained results by utilizing this technique can be used to analyze trends, forecast of development and might be helpful for choosing most efficient scenarios for sustainable spatial planning and land management.
