To minimize the low classification accuracy and low utilization of spatial information in traditional hyperspectral image classification methods, we propose a new hyperspectral image classification method, which is ba...To minimize the low classification accuracy and low utilization of spatial information in traditional hyperspectral image classification methods, we propose a new hyperspectral image classification method, which is based on the Gabor spatial texture features and nonparametric weighted spectral features, and the sparse representation classification method（Gabor–NWSF and SRC）, abbreviated GNWSF–SRC. The proposed（GNWSF–SRC） method first combines the Gabor spatial features and nonparametric weighted spectral features to describe the hyperspectral image, and then applies the sparse representation method. Finally, the classification is obtained by analyzing the reconstruction error. We use the proposed method to process two typical hyperspectral data sets with different percentages of training samples. Theoretical analysis and simulation demonstrate that the proposed method improves the classification accuracy and Kappa coefficient compared with traditional classification methods and achieves better classification performance.展开更多
This study focused on land cover mapping based on synthetic images,especially using the method of spatial and temporal classification as well as the accuracy validation of their results.Our experimental results indica...This study focused on land cover mapping based on synthetic images,especially using the method of spatial and temporal classification as well as the accuracy validation of their results.Our experimental results indicate that the accuracy of land cover map based on synthetic imagery and actual observation has a similar standard compared with actual land cover survey data.These findings facilitate land cover mapping with synthetic data in the area where actual observation is missing.Furthermore,in order to improve the quality of the land cover mapping,this research employed the spatial and temporal Markov random field classification approach.Test results show that overall mapping accuracy can be increased by approximately 5% after applying spatial and temporal classification.This finding contributes towards the achievement of higher quality land cover mapping of areas with missing data by using spatial and temporal information.展开更多
We describe how the Unit-Feature Spatial Classification Method(UFSCM) can be used operationally to classify cloud types in satellite imagery efficiently and conveniently.By using a combination of Interactive Data Lang...We describe how the Unit-Feature Spatial Classification Method(UFSCM) can be used operationally to classify cloud types in satellite imagery efficiently and conveniently.By using a combination of Interactive Data Language(IDL) and Visual C++(VC) code in combination to extend the technique in three dimensions(3-D),this paper provides an efficient method to implement interactive computer visualization of the 3-D discrimination matrix modification,so as to deal with the bi-spectral limitations of traditional two dimensional(2-D) UFSCM.The case study of cloud-type classification based on FY-2C satellite data (0600 UTC 18 and 0000 UTC 10 September 2007) is conducted by comparison with ground station data, and indicates that 3-D UFSCM makes more use of the pattern recognition information in multi-spectral imagery,resulting in more reasonable results and an improvement over the 2-D method.展开更多
High Spatial and Spectral Resolution(HSSR)remote-sensing images can provide rich spectral bands and detailed ground information,but there is a relative lack of research on this new type of remote-sensing data.Although...High Spatial and Spectral Resolution(HSSR)remote-sensing images can provide rich spectral bands and detailed ground information,but there is a relative lack of research on this new type of remote-sensing data.Although there are already some HSSR datasets for deep learning model training and testing,the data volume of these datasets is small,resulting in low classification accuracy and weak generalization ability of the trained models.In this paper,an HSSR dataset Luojia-HSSR is constructed based on aerial hyperspectral imagery of southern Shenyang City of Liaoning Province in China.To our knowledge,it is the largest HSSR dataset to date,with 6438 pairs of 256×256 sized samples(including 3480 pairs in the training set,2209 pairs in the test set,and 749 pairs in the validation set),covering area of 161 km2 with spatial resolution 0.75 m,249 Visible and Near-Infrared(VNIR)spectral bands,and corresponding to 23 classes of field-validated ground coverage.It is an ideal experimental data for spatial-spectral feature extraction.Furthermore,a new deep learning model 3D-HRNet for interpreting HSSR images is proposed.The conv-neck in HRNet is modified to better mine the spatial information of the images.Then,a 3D convolution module with attention mechanism is designed to capture the global-local fine spectral information simultaneously.Subsequently,the 3D convolution is inserted into the HRNet to optimize the performance.The experiments show that the 3D-HRNet model has good interpreting ability for the Luojia-HSSR dataset with the Frequency Weighted Intersection over Union(FWIoU)reaching 80.54%,indicating that the Luojia-HSSR dataset constructed in this paper and the proposed 3D-HRnet model have good applicable prospects for processing HSSR remote sensing images.展开更多
This paper focuses on the problem of automatic image classification （AIC） by proposing a framework based on latent semantic analysis （LSA） and image region pairs. The novel framework employs relative spatial arran...This paper focuses on the problem of automatic image classification （AIC） by proposing a framework based on latent semantic analysis （LSA） and image region pairs. The novel framework employs relative spatial arrangements for region pairs as the primary feature to capture semantics. The significance of this paper is twofold. Firstly, to the best our knowledge, this is the first study of the influence of region pairs as well as their relative spatial information in latent semantic analysis as applied to automatic image classification. Secondly, our proposed method for using the relative spatial information of region pairs show great promise in improving image semantic classi- fication compared with the classical latent semantic analysis method and 2D string representation algorithm.展开更多
A comprehensive assessment of the spatial.aware mpervised learning algorithms for hyper.spectral image （HSI） classification was presented. For this purpose, standard support vector machines （ SVMs ）, mudttnomial l...A comprehensive assessment of the spatial.aware mpervised learning algorithms for hyper.spectral image （HSI） classification was presented. For this purpose, standard support vector machines （ SVMs ）, mudttnomial logistic regression （ MLR ） and sparse representation （SR） based supervised learning algorithm were compared both theoretically and experimentally. Performance of the discussed techniques was evaluated in terms of overall accuracy, average accuracy, kappa statistic coefficients, and sparsity of the solutions. Execution time, the computational burden, and the capability of the methods were investigated by using probabilistie analysis. For validating the accuracy a classical benchmark AVIRIS Indian pines data set was used. Experiments show that integrating spectral.spatial context can further improve the accuracy, reduce the misclassltication error although the cost of computational time will be increased.展开更多
The important strategic metal cobalt has diverse uses and the majority of world cobalt deposits have been found in China. The deposits can be classified into four types, i.e., magmatic Ni-Cu-Co sulfide deposits, hydro...The important strategic metal cobalt has diverse uses and the majority of world cobalt deposits have been found in China. The deposits can be classified into four types, i.e., magmatic Ni-Cu-Co sulfide deposits, hydrothermal and volcanogenic cobalt polymetallic deposits, strata-bound Cu-Co deposits hosted by sedimentary rocks and lateritic Ni-Co deposits, of which the former two types are the most important. There are six principal metallogenic epochs and seven important metallogenic belts according to their distribution and tectonic position. Although cobalt generally occurs in nickel-copper, copper and iron deposits as an associated metal, great developments in exploration for independent cobalt deposits have happened in China, and, in recent years, many independent deposits with different elementary assemblages and different genetic types have been discovered in the eastern part of the northern margin of the North China platform, the Central Orogenic Belt of China, western Jiangxi and northeastern Hunan. In addition, it is inferred that the Kunlun-Qinling Orogenic Belt has great potential for further exploration of new types of independent cobalt deposits.展开更多
Wetland research has become a hot spot linking multiple disciplines presently. Wetland classification and mapping is the basis for wetland research. It is difficult to generate wetland data sets using traditional meth...Wetland research has become a hot spot linking multiple disciplines presently. Wetland classification and mapping is the basis for wetland research. It is difficult to generate wetland data sets using traditional methods because of the low accessibility of wetlands, hence remote sensing data have become one of the primary data sources in wetland research. This paper presents a case study conducted at the core area of Honghe National Nature Reserve in the Sanjiang Plain, Northeast China. In this study, three images generated by airship, from Thematic Mapper and from SPOT 5 were selected to produce wetland maps at three different wetland landscape levels. After assessing classification accuracies of the three maps, we compared the different wetland mapping results of 11 plant communities to the airship image, 6 plant ecotypes to the TM image and 9 landscape classifications to the SPOT 5 image. We discussed the different characteristics of the hierarchical ecosystem classifications based on the spatial scales of the different images. The results indicate that spatial scales of remote sensing data have an important link to the hierarchies of wetland plant ecosystems displayed on the wetland landscape maps. The richness of wetland landscape information derived from an image closely relates to its spatial resolution. This study can enrich the ecological classification methods and mapping techniques dealing with the spatial scales of different remote sensing images. With a better understanding of classification accuracies in mapping wetlands by using different scales of remote sensing data, we can make an appropriate approach for dealing with the scale issue of remote sensing images.展开更多
Accurate crop distribution mapping is required for crop yield prediction and field management. Due to rapid progress in remote sensing technology, fine spatial resolution(FSR) remotely sensed imagery now offers great ...Accurate crop distribution mapping is required for crop yield prediction and field management. Due to rapid progress in remote sensing technology, fine spatial resolution(FSR) remotely sensed imagery now offers great opportunities for mapping crop types in great detail. However, within-class variance can hamper attempts to discriminate crop classes at fine resolutions. Multi-temporal FSR remotely sensed imagery provides a means of increasing crop classification from FSR imagery, although current methods do not exploit the available information fully. In this research, a novel Temporal Sequence Object-based Convolutional Neural Network(TS-OCNN) was proposed to classify agricultural crop type from FSR image time-series. An object-based CNN(OCNN) model was adopted in the TS-OCNN to classify images at the object level(i.e., segmented objects or crop parcels), thus, maintaining the precise boundary information of crop parcels. The combination of image time-series was first utilized as the input to the OCNN model to produce an ‘original’ or baseline classification. Then the single-date images were fed automatically into the deep learning model scene-by-scene in order of image acquisition date to increase successively the crop classification accuracy. By doing so, the joint information in the FSR multi-temporal observations and the unique individual information from the single-date images were exploited comprehensively for crop classification. The effectiveness of the proposed approach was investigated using multitemporal SAR and optical imagery, respectively, over two heterogeneous agricultural areas. The experimental results demonstrated that the newly proposed TS-OCNN approach consistently increased crop classification accuracy, and achieved the greatest accuracies(82.68% and 87.40%) in comparison with state-of-the-art benchmark methods, including the object-based CNN(OCNN)(81.63% and85.88%), object-based image analysis(OBIA)(78.21% and 84.83%), and standard pixel-wise CNN(79.18%and 82.90%). The proposed approach is the first known attempt to explore simultaneously the joint information from image time-series with the unique information from single-date images for crop classification using a deep learning framework. The TS-OCNN, therefore, represents a new approach for agricultural landscape classification from multi-temporal FSR imagery. Besides, it is readily generalizable to other landscapes(e.g., forest landscapes), with a wide application prospect.展开更多
The remote sensing image classification has stimulated considerable interest as an effective method for better retrieving information from the rapidly increasing large volume, complex and distributed satellite remote ...The remote sensing image classification has stimulated considerable interest as an effective method for better retrieving information from the rapidly increasing large volume, complex and distributed satellite remote imaging data of large scale and cross-time, due to the increase of remote image quantities and image resolutions. In the paper, the genetic algorithms were employed to solve the weighting of the radial basis faction networks in order to improve the precision of remote sensing image classification. The remote sensing image classification was also introduced for the GIS spatial analysis and the spatial online analytical processing (OLAP), and the resulted effectiveness was demonstrated in the analysis of land utilization variation of Daqing city.展开更多
The aim of the study is to classify the Sandstorms according to year seasons as well as their spatial variation in Saudi Arabia. Factor analysis has been used for data collection. Three factors have been presented: th...The aim of the study is to classify the Sandstorms according to year seasons as well as their spatial variation in Saudi Arabia. Factor analysis has been used for data collection. Three factors have been presented: the first factor related to Spring as a prime Season for Dust Sandstorms. Factor two shows that Samar months concern as ascend season for Sandstorms, while the Autumn Season comes as a third period for Dust Sandstorms. With regard to spatial variation, Al-Ahsa station came as the most closely related station in the spring season, followed by Hafr Elbatten, Jazan and Al-Jouf stations, while Jazan and Yenbo stations were the most connected stations in the summer season, Turaif is more closely related, to Fall season. By the end of the study several results and recommendations have been addressed.展开更多
The evolving field of Alzheimer’s disease(AD)diagnosis has greatly benefited from deep learning models for analyzing brain magnetic resonance(MR)images.This study introduces Dynamic GradNet,a novel deep learning mode...The evolving field of Alzheimer’s disease(AD)diagnosis has greatly benefited from deep learning models for analyzing brain magnetic resonance(MR)images.This study introduces Dynamic GradNet,a novel deep learning model designed to increase diagnostic accuracy and interpretability for multiclass AD classification.Initially,four state-of-the-art convolutional neural network(CNN)architectures,the self-regulated network(RegNet),residual network(ResNet),densely connected convolutional network(DenseNet),and efficient network(EfficientNet),were comprehensively compared via a unified preprocessing pipeline to ensure a fair evaluation.Among these models,EfficientNet consistently demonstrated superior performance in terms of accuracy,precision,recall,and F1 score.As a result,EfficientNetwas selected as the foundation for implementing Dynamic GradNet.Dynamic GradNet incorporates gradient weighted class activation mapping(GradCAM)into the training process,facilitating dynamic adjustments that focus on critical brain regions associated with early dementia detection.These adjustments are particularly effective in identifying subtle changes associated with very mild dementia,enabling early diagnosis and intervention.The model was evaluated with the OASIS dataset,which contains greater than 80,000 brain MR images categorized into four distinct stages of AD progression.The proposed model outperformed the baseline architectures,achieving remarkable generalizability across all stages.This findingwas especially evident in early-stage dementia detection,where Dynamic GradNet significantly reduced false positives and enhanced classification metrics.These findings highlight the potential of Dynamic GradNet as a robust and scalable approach for AD diagnosis,providing a promising alternative to traditional attention-based models.The model’s ability to dynamically adjust spatial focus offers a powerful tool in artificial intelligence(AI)assisted precisionmedicine,particularly in the early detection of neurodegenerative diseases.展开更多
Natural geological conditions and geo-tourism resources in Jilin Province were introduced,and distribution features of the local major tourist resources(vegetation-covered eastern region,grass swamp on western plain) ...Natural geological conditions and geo-tourism resources in Jilin Province were introduced,and distribution features of the local major tourist resources(vegetation-covered eastern region,grass swamp on western plain) were studied.Jingyu Volcanic Mineral Spring Geo-park,Changbai Mountain Geo-park and Qian'an Mud Forest Geo-park were studied as typical geo-tourism resources,so as to provide basic data for the systematic development and construction of geo-tourism resources in Jilin Province.展开更多
Understanding the spatial distributions and corresponding variation mechanisms of key soil nutrients in fragile karst ecosystems can assist in promoting sustainable development.However,due to the implementation of eco...Understanding the spatial distributions and corresponding variation mechanisms of key soil nutrients in fragile karst ecosystems can assist in promoting sustainable development.However,due to the implementation of ecological restoration initiatives such as land-use conversions,novel changes in the spatial characteristics of soil nutrients remain unknown.To address this gap,we explored nutrient variations and the drivers of the variation in the 0–15 cm topsoil layer using a regional-scale sampling method in a typical karst area in northwest Guangxi Zhuang Autonomous Region,Southwest China.Descriptive statistics,geostatistics,and spatial analysis were used to assess the soil nutrient variability.The results indicated that soil organic carbon(SOC),total nitrogen(TN),total phosphorus(TP),and total potassium(TK)concentrations showed moderate variations,with coefficients of variance being 0.60,0.60,0.71,and 0.72,respectively.Moreover,they demonstrated positive spatial autocorrelations,with global Moran's indices being 0.68,0.77,0.64,and 0.68,respectively.However,local Moran's index values were low,indicating large spatial variations in soil nutrients.The best-fitting semi-variogram models for SOC,TN,TP,and TK concentrations were spherical,Gaussian,exponential,and exponential,respectively.According to the classification criteria of the Second National Soil Census in China,SOC and TN concentrations were relatively sufficient,with the proportions of rich and very rich levels being up to 90.9 and 96.0%,respectively.TP concentration was in the mediumdeficient level,with the areas of medium and deficient levels accounting for 33.7 and 30.1%of the total,respectively.TK concentration was deficient,with the cumulative area of extremely deficient,very deficient,and deficient levels accounting for 87.6%of the total area.Consequently,the terrestrial ecosystems in the study area were more vulnerable to soil P and K than soil N deficiencies.Furthermore,variance partitioning analysis of the influencing factors showed that,except for the interactions,the single effect of other soil properties accounted more for soil nutrient variations than spatial and environmental variables.These results will aid in the future management of terrestrial ecosystems.展开更多
Fuzzy classification combined with spatial prediction was used to assess the state of soil pollution in the peri-urban Beijing area. Total concentrations of As, Cr, Cd, Hg, and Pb were determined in 220 topsoil sampl...Fuzzy classification combined with spatial prediction was used to assess the state of soil pollution in the peri-urban Beijing area. Total concentrations of As, Cr, Cd, Hg, and Pb were determined in 220 topsoil samples （0-20 cm） collected using a grid design in a study area of 2 600 kin2. Heavy metal concentrations were grouped into three classes according to the optimum number of classes and fuzziness exponent using the fuzzy comean （FCM） algorithm. Membership values were interpolated using ordinary kriging. The polluted soils of the study area induced by the measured heavy metals were concentrated in the northwest corner and eastern part, especially the southeastern part close to the urban zone, whereas the soils free of pollution were mainly distributed in the southwestern part. The soils with potential risk of heavy metal pollution were located in isolated spots mainly in the northern part and southeastern corner of the study region. The FCM algorithm combined with geostatistical techniques, as compared to conventional single geostatistical kriging methods, could produce a prediction with a quantitative uncertainty evaluation and higher reliability. Successful prediction of soil pollution achieved with FCM algorithm in this study indicated that fuzzy set theory had great potential for use in other areas of soil science.展开更多
This study analyzes the impact of circulation types(CTs)on ozone(O_(3))pollution in Beijing.The easterly high-pressure(SWW)circulation occurred most frequently(30%;276 day),followed by northwesterly high-pressure(AN)c...This study analyzes the impact of circulation types(CTs)on ozone(O_(3))pollution in Beijing.The easterly high-pressure(SWW)circulation occurred most frequently(30%;276 day),followed by northwesterly high-pressure(AN)circulation(24.3%;224 day).The SWW type had the highest O_(3) anomaly of+17.28μg/m^(3),which was caused by excellent photochemical reactions,poor diffusion ability and regional transport.Due to the higher humidity and precipitation in the low-pressure type(C),the O_(3) increase(+8.02μg/m^(3))was less than that in the SWW type.Good diffusion/wet deposition and weak formation ability contributed to O_(3) decrease in AN(-12.54μg/m^(3))and northerly high-pressure(ESN)CTs(-12.26μg/m^(3)).The intra-area transport of O_(3) was significant in polluted circulations(SWW-and C-CTs).In addition,higher temperature,radiation and less rainfall also contributed to higher O_(3) in northern Beijing under the SWW type.For the clean CTs(AN and ESN CTs),precursor amount and intra-area transport played a dominant role in O_(3) distribution.Under the northeasterly low-pressure CT,better formation conditions and higher precursor amount combined with the intra-area southerly transport to cause higher O_(3) values in the south than in the north.The higher O_(3) in the northwestern area under the northeasterly high-pressure type was influenced by weaker titration loss and high O_(3) concentration in previous day.Annual variation in the CTs contributed up to 86.1%of the annual variation in O_(3).About 78%-83%of the diurnal variation in O_(3) resulted from local meteorological factors.展开更多
基金supported by the National Natural Science Foundation of China(No.61275010)the Ph.D.Programs Foundation of Ministry of Education of China(No.20132304110007)+1 种基金the Heilongjiang Natural Science Foundation(No.F201409)the Fundamental Research Funds for the Central Universities(No.HEUCFD1410)
文摘To minimize the low classification accuracy and low utilization of spatial information in traditional hyperspectral image classification methods, we propose a new hyperspectral image classification method, which is based on the Gabor spatial texture features and nonparametric weighted spectral features, and the sparse representation classification method（Gabor–NWSF and SRC）, abbreviated GNWSF–SRC. The proposed（GNWSF–SRC） method first combines the Gabor spatial features and nonparametric weighted spectral features to describe the hyperspectral image, and then applies the sparse representation method. Finally, the classification is obtained by analyzing the reconstruction error. We use the proposed method to process two typical hyperspectral data sets with different percentages of training samples. Theoretical analysis and simulation demonstrate that the proposed method improves the classification accuracy and Kappa coefficient compared with traditional classification methods and achieves better classification performance.
基金supported in part by the National High-Tech R&D Program(863 program)under grant number 2009AA122004the National Natural Science Foundation of China under grant number 60171009the Hong Kong Research Grant Council under grant number CUHK 444612.
文摘This study focused on land cover mapping based on synthetic images,especially using the method of spatial and temporal classification as well as the accuracy validation of their results.Our experimental results indicate that the accuracy of land cover map based on synthetic imagery and actual observation has a similar standard compared with actual land cover survey data.These findings facilitate land cover mapping with synthetic data in the area where actual observation is missing.Furthermore,in order to improve the quality of the land cover mapping,this research employed the spatial and temporal Markov random field classification approach.Test results show that overall mapping accuracy can be increased by approximately 5% after applying spatial and temporal classification.This finding contributes towards the achievement of higher quality land cover mapping of areas with missing data by using spatial and temporal information.
基金supported by the National Natural Science Foundation of China(Grant No.40875012)the National Basic Research Program of China(Grant No.2009CB421502)the Meteorology Open Fund of Huaihe River Basin(HRM200704).
文摘We describe how the Unit-Feature Spatial Classification Method(UFSCM) can be used operationally to classify cloud types in satellite imagery efficiently and conveniently.By using a combination of Interactive Data Language(IDL) and Visual C++(VC) code in combination to extend the technique in three dimensions(3-D),this paper provides an efficient method to implement interactive computer visualization of the 3-D discrimination matrix modification,so as to deal with the bi-spectral limitations of traditional two dimensional(2-D) UFSCM.The case study of cloud-type classification based on FY-2C satellite data (0600 UTC 18 and 0000 UTC 10 September 2007) is conducted by comparison with ground station data, and indicates that 3-D UFSCM makes more use of the pattern recognition information in multi-spectral imagery,resulting in more reasonable results and an improvement over the 2-D method.
基金supported by the Major Program of the National Natural Science Foundation of China[grant number 92038301]The research was also supported by the National Natural Science Foundation of China[grant number 41971295]+1 种基金the Foundation for Innovative Research Groups of the Natural Science Foundation of Hubei Province[grant number 2020CFA003]the Special Fund of Hubei Luojia Laboratory.
文摘High Spatial and Spectral Resolution(HSSR)remote-sensing images can provide rich spectral bands and detailed ground information,but there is a relative lack of research on this new type of remote-sensing data.Although there are already some HSSR datasets for deep learning model training and testing,the data volume of these datasets is small,resulting in low classification accuracy and weak generalization ability of the trained models.In this paper,an HSSR dataset Luojia-HSSR is constructed based on aerial hyperspectral imagery of southern Shenyang City of Liaoning Province in China.To our knowledge,it is the largest HSSR dataset to date,with 6438 pairs of 256×256 sized samples(including 3480 pairs in the training set,2209 pairs in the test set,and 749 pairs in the validation set),covering area of 161 km2 with spatial resolution 0.75 m,249 Visible and Near-Infrared(VNIR)spectral bands,and corresponding to 23 classes of field-validated ground coverage.It is an ideal experimental data for spatial-spectral feature extraction.Furthermore,a new deep learning model 3D-HRNet for interpreting HSSR images is proposed.The conv-neck in HRNet is modified to better mine the spatial information of the images.Then,a 3D convolution module with attention mechanism is designed to capture the global-local fine spectral information simultaneously.Subsequently,the 3D convolution is inserted into the HRNet to optimize the performance.The experiments show that the 3D-HRNet model has good interpreting ability for the Luojia-HSSR dataset with the Frequency Weighted Intersection over Union(FWIoU)reaching 80.54%,indicating that the Luojia-HSSR dataset constructed in this paper and the proposed 3D-HRnet model have good applicable prospects for processing HSSR remote sensing images.
文摘This paper focuses on the problem of automatic image classification （AIC） by proposing a framework based on latent semantic analysis （LSA） and image region pairs. The novel framework employs relative spatial arrangements for region pairs as the primary feature to capture semantics. The significance of this paper is twofold. Firstly, to the best our knowledge, this is the first study of the influence of region pairs as well as their relative spatial information in latent semantic analysis as applied to automatic image classification. Secondly, our proposed method for using the relative spatial information of region pairs show great promise in improving image semantic classi- fication compared with the classical latent semantic analysis method and 2D string representation algorithm.
基金National Key Research and Development Program of China(No.2016YFF0103604)National Natural Science Foundations of China(Nos.61171165,11431015,61571230)+1 种基金National Scientific Equipment Developing Project of China(No.2012YQ050250)Natural Science Foundation of Jiangsu Province,China(No.BK20161500)
文摘A comprehensive assessment of the spatial.aware mpervised learning algorithms for hyper.spectral image （HSI） classification was presented. For this purpose, standard support vector machines （ SVMs ）, mudttnomial logistic regression （ MLR ） and sparse representation （SR） based supervised learning algorithm were compared both theoretically and experimentally. Performance of the discussed techniques was evaluated in terms of overall accuracy, average accuracy, kappa statistic coefficients, and sparsity of the solutions. Execution time, the computational burden, and the capability of the methods were investigated by using probabilistie analysis. For validating the accuracy a classical benchmark AVIRIS Indian pines data set was used. Experiments show that integrating spectral.spatial context can further improve the accuracy, reduce the misclassltication error although the cost of computational time will be increased.
基金financially supported jointly by a project of the National Natural Science Foundation of China(Grant 40302019) China Geological Survey project DKD2001027-4.
文摘The important strategic metal cobalt has diverse uses and the majority of world cobalt deposits have been found in China. The deposits can be classified into four types, i.e., magmatic Ni-Cu-Co sulfide deposits, hydrothermal and volcanogenic cobalt polymetallic deposits, strata-bound Cu-Co deposits hosted by sedimentary rocks and lateritic Ni-Co deposits, of which the former two types are the most important. There are six principal metallogenic epochs and seven important metallogenic belts according to their distribution and tectonic position. Although cobalt generally occurs in nickel-copper, copper and iron deposits as an associated metal, great developments in exploration for independent cobalt deposits have happened in China, and, in recent years, many independent deposits with different elementary assemblages and different genetic types have been discovered in the eastern part of the northern margin of the North China platform, the Central Orogenic Belt of China, western Jiangxi and northeastern Hunan. In addition, it is inferred that the Kunlun-Qinling Orogenic Belt has great potential for further exploration of new types of independent cobalt deposits.
基金Under the auspices of National Natural Science Foundation of China (No. 40871241, 40771170)National High Technology Research and Development Program of China (No. 2007AA12Z176)
文摘Wetland research has become a hot spot linking multiple disciplines presently. Wetland classification and mapping is the basis for wetland research. It is difficult to generate wetland data sets using traditional methods because of the low accessibility of wetlands, hence remote sensing data have become one of the primary data sources in wetland research. This paper presents a case study conducted at the core area of Honghe National Nature Reserve in the Sanjiang Plain, Northeast China. In this study, three images generated by airship, from Thematic Mapper and from SPOT 5 were selected to produce wetland maps at three different wetland landscape levels. After assessing classification accuracies of the three maps, we compared the different wetland mapping results of 11 plant communities to the airship image, 6 plant ecotypes to the TM image and 9 landscape classifications to the SPOT 5 image. We discussed the different characteristics of the hierarchical ecosystem classifications based on the spatial scales of the different images. The results indicate that spatial scales of remote sensing data have an important link to the hierarchies of wetland plant ecosystems displayed on the wetland landscape maps. The richness of wetland landscape information derived from an image closely relates to its spatial resolution. This study can enrich the ecological classification methods and mapping techniques dealing with the spatial scales of different remote sensing images. With a better understanding of classification accuracies in mapping wetlands by using different scales of remote sensing data, we can make an appropriate approach for dealing with the scale issue of remote sensing images.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA28070503)the National Key Research and Development Program of China(2021YFD1500100)+2 种基金the Open Fund of State Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University (20R04)Land Observation Satellite Supporting Platform of National Civil Space Infrastructure Project(CASPLOS-CCSI)a PhD studentship ‘‘Deep Learning in massive area,multi-scale resolution remotely sensed imagery”(EAA7369),sponsored by Lancaster University and Ordnance Survey (the national mapping agency of Great Britain)。
文摘Accurate crop distribution mapping is required for crop yield prediction and field management. Due to rapid progress in remote sensing technology, fine spatial resolution(FSR) remotely sensed imagery now offers great opportunities for mapping crop types in great detail. However, within-class variance can hamper attempts to discriminate crop classes at fine resolutions. Multi-temporal FSR remotely sensed imagery provides a means of increasing crop classification from FSR imagery, although current methods do not exploit the available information fully. In this research, a novel Temporal Sequence Object-based Convolutional Neural Network(TS-OCNN) was proposed to classify agricultural crop type from FSR image time-series. An object-based CNN(OCNN) model was adopted in the TS-OCNN to classify images at the object level(i.e., segmented objects or crop parcels), thus, maintaining the precise boundary information of crop parcels. The combination of image time-series was first utilized as the input to the OCNN model to produce an ‘original’ or baseline classification. Then the single-date images were fed automatically into the deep learning model scene-by-scene in order of image acquisition date to increase successively the crop classification accuracy. By doing so, the joint information in the FSR multi-temporal observations and the unique individual information from the single-date images were exploited comprehensively for crop classification. The effectiveness of the proposed approach was investigated using multitemporal SAR and optical imagery, respectively, over two heterogeneous agricultural areas. The experimental results demonstrated that the newly proposed TS-OCNN approach consistently increased crop classification accuracy, and achieved the greatest accuracies(82.68% and 87.40%) in comparison with state-of-the-art benchmark methods, including the object-based CNN(OCNN)(81.63% and85.88%), object-based image analysis(OBIA)(78.21% and 84.83%), and standard pixel-wise CNN(79.18%and 82.90%). The proposed approach is the first known attempt to explore simultaneously the joint information from image time-series with the unique information from single-date images for crop classification using a deep learning framework. The TS-OCNN, therefore, represents a new approach for agricultural landscape classification from multi-temporal FSR imagery. Besides, it is readily generalizable to other landscapes(e.g., forest landscapes), with a wide application prospect.
基金Sponsored by the National Natural Science Foundation of China (Grant No.40271044), Natural Science Foundation(Grant No.TK2005 -17) and Projectof Science Backbone of Heilongjiang Province(Grant No.1151G021).
文摘The remote sensing image classification has stimulated considerable interest as an effective method for better retrieving information from the rapidly increasing large volume, complex and distributed satellite remote imaging data of large scale and cross-time, due to the increase of remote image quantities and image resolutions. In the paper, the genetic algorithms were employed to solve the weighting of the radial basis faction networks in order to improve the precision of remote sensing image classification. The remote sensing image classification was also introduced for the GIS spatial analysis and the spatial online analytical processing (OLAP), and the resulted effectiveness was demonstrated in the analysis of land utilization variation of Daqing city.
文摘The aim of the study is to classify the Sandstorms according to year seasons as well as their spatial variation in Saudi Arabia. Factor analysis has been used for data collection. Three factors have been presented: the first factor related to Spring as a prime Season for Dust Sandstorms. Factor two shows that Samar months concern as ascend season for Sandstorms, while the Autumn Season comes as a third period for Dust Sandstorms. With regard to spatial variation, Al-Ahsa station came as the most closely related station in the spring season, followed by Hafr Elbatten, Jazan and Al-Jouf stations, while Jazan and Yenbo stations were the most connected stations in the summer season, Turaif is more closely related, to Fall season. By the end of the study several results and recommendations have been addressed.
基金funded by Taif University,Saudi ArabiaThe author would like to acknowledge Deanship of Graduate Studies and Scientific Research,Taif University for funding this work.
文摘The evolving field of Alzheimer’s disease(AD)diagnosis has greatly benefited from deep learning models for analyzing brain magnetic resonance(MR)images.This study introduces Dynamic GradNet,a novel deep learning model designed to increase diagnostic accuracy and interpretability for multiclass AD classification.Initially,four state-of-the-art convolutional neural network(CNN)architectures,the self-regulated network(RegNet),residual network(ResNet),densely connected convolutional network(DenseNet),and efficient network(EfficientNet),were comprehensively compared via a unified preprocessing pipeline to ensure a fair evaluation.Among these models,EfficientNet consistently demonstrated superior performance in terms of accuracy,precision,recall,and F1 score.As a result,EfficientNetwas selected as the foundation for implementing Dynamic GradNet.Dynamic GradNet incorporates gradient weighted class activation mapping(GradCAM)into the training process,facilitating dynamic adjustments that focus on critical brain regions associated with early dementia detection.These adjustments are particularly effective in identifying subtle changes associated with very mild dementia,enabling early diagnosis and intervention.The model was evaluated with the OASIS dataset,which contains greater than 80,000 brain MR images categorized into four distinct stages of AD progression.The proposed model outperformed the baseline architectures,achieving remarkable generalizability across all stages.This findingwas especially evident in early-stage dementia detection,where Dynamic GradNet significantly reduced false positives and enhanced classification metrics.These findings highlight the potential of Dynamic GradNet as a robust and scalable approach for AD diagnosis,providing a promising alternative to traditional attention-based models.The model’s ability to dynamically adjust spatial focus offers a powerful tool in artificial intelligence(AI)assisted precisionmedicine,particularly in the early detection of neurodegenerative diseases.
基金Supported by Social Development Foundation of Jilin Provincial Department of Science and Technology(20090478)~~
文摘Natural geological conditions and geo-tourism resources in Jilin Province were introduced,and distribution features of the local major tourist resources(vegetation-covered eastern region,grass swamp on western plain) were studied.Jingyu Volcanic Mineral Spring Geo-park,Changbai Mountain Geo-park and Qian'an Mud Forest Geo-park were studied as typical geo-tourism resources,so as to provide basic data for the systematic development and construction of geo-tourism resources in Jilin Province.
基金supported by the National Natural Science Foundation of China(U2344201 and 42101316)the Natural Science Foundation of Hunan Province,China(2022JJ40866)the Outstanding Youth Project of Education Bureau of Hunan Province,China(20B613)。
文摘Understanding the spatial distributions and corresponding variation mechanisms of key soil nutrients in fragile karst ecosystems can assist in promoting sustainable development.However,due to the implementation of ecological restoration initiatives such as land-use conversions,novel changes in the spatial characteristics of soil nutrients remain unknown.To address this gap,we explored nutrient variations and the drivers of the variation in the 0–15 cm topsoil layer using a regional-scale sampling method in a typical karst area in northwest Guangxi Zhuang Autonomous Region,Southwest China.Descriptive statistics,geostatistics,and spatial analysis were used to assess the soil nutrient variability.The results indicated that soil organic carbon(SOC),total nitrogen(TN),total phosphorus(TP),and total potassium(TK)concentrations showed moderate variations,with coefficients of variance being 0.60,0.60,0.71,and 0.72,respectively.Moreover,they demonstrated positive spatial autocorrelations,with global Moran's indices being 0.68,0.77,0.64,and 0.68,respectively.However,local Moran's index values were low,indicating large spatial variations in soil nutrients.The best-fitting semi-variogram models for SOC,TN,TP,and TK concentrations were spherical,Gaussian,exponential,and exponential,respectively.According to the classification criteria of the Second National Soil Census in China,SOC and TN concentrations were relatively sufficient,with the proportions of rich and very rich levels being up to 90.9 and 96.0%,respectively.TP concentration was in the mediumdeficient level,with the areas of medium and deficient levels accounting for 33.7 and 30.1%of the total,respectively.TK concentration was deficient,with the cumulative area of extremely deficient,very deficient,and deficient levels accounting for 87.6%of the total area.Consequently,the terrestrial ecosystems in the study area were more vulnerable to soil P and K than soil N deficiencies.Furthermore,variance partitioning analysis of the influencing factors showed that,except for the interactions,the single effect of other soil properties accounted more for soil nutrient variations than spatial and environmental variables.These results will aid in the future management of terrestrial ecosystems.
基金Project supported by the National Natural Science Foundation of China (Nos. 40571065 and 40235054)the National Key Basic Research Support Foundation of China (No. G1999045707).
文摘Fuzzy classification combined with spatial prediction was used to assess the state of soil pollution in the peri-urban Beijing area. Total concentrations of As, Cr, Cd, Hg, and Pb were determined in 220 topsoil samples （0-20 cm） collected using a grid design in a study area of 2 600 kin2. Heavy metal concentrations were grouped into three classes according to the optimum number of classes and fuzziness exponent using the fuzzy comean （FCM） algorithm. Membership values were interpolated using ordinary kriging. The polluted soils of the study area induced by the measured heavy metals were concentrated in the northwest corner and eastern part, especially the southeastern part close to the urban zone, whereas the soils free of pollution were mainly distributed in the southwestern part. The soils with potential risk of heavy metal pollution were located in isolated spots mainly in the northern part and southeastern corner of the study region. The FCM algorithm combined with geostatistical techniques, as compared to conventional single geostatistical kriging methods, could produce a prediction with a quantitative uncertainty evaluation and higher reliability. Successful prediction of soil pollution achieved with FCM algorithm in this study indicated that fuzzy set theory had great potential for use in other areas of soil science.
基金supported by the Beijing Municipal Natural Science Foundation(No.8204075)the National Key Research and Development Program of China(No.2016YFC0203302)+2 种基金the National Natural Science Foundation of China(Nos.4147513591744206)the Beijing Nova Program(No.xx2017079).
文摘This study analyzes the impact of circulation types(CTs)on ozone(O_(3))pollution in Beijing.The easterly high-pressure(SWW)circulation occurred most frequently(30%;276 day),followed by northwesterly high-pressure(AN)circulation(24.3%;224 day).The SWW type had the highest O_(3) anomaly of+17.28μg/m^(3),which was caused by excellent photochemical reactions,poor diffusion ability and regional transport.Due to the higher humidity and precipitation in the low-pressure type(C),the O_(3) increase(+8.02μg/m^(3))was less than that in the SWW type.Good diffusion/wet deposition and weak formation ability contributed to O_(3) decrease in AN(-12.54μg/m^(3))and northerly high-pressure(ESN)CTs(-12.26μg/m^(3)).The intra-area transport of O_(3) was significant in polluted circulations(SWW-and C-CTs).In addition,higher temperature,radiation and less rainfall also contributed to higher O_(3) in northern Beijing under the SWW type.For the clean CTs(AN and ESN CTs),precursor amount and intra-area transport played a dominant role in O_(3) distribution.Under the northeasterly low-pressure CT,better formation conditions and higher precursor amount combined with the intra-area southerly transport to cause higher O_(3) values in the south than in the north.The higher O_(3) in the northwestern area under the northeasterly high-pressure type was influenced by weaker titration loss and high O_(3) concentration in previous day.Annual variation in the CTs contributed up to 86.1%of the annual variation in O_(3).About 78%-83%of the diurnal variation in O_(3) resulted from local meteorological factors.
