Due to the volume conduction,electroencephalogram（EEG） gives a rather blurred image of brain activities. It is a challenge for generating satisfactory performance with EEG. This paper studies the multiple areas fusi...Due to the volume conduction,electroencephalogram（EEG） gives a rather blurred image of brain activities. It is a challenge for generating satisfactory performance with EEG. This paper studies the multiple areas fusion of EEG classifiers to improve the motor imagery EEG classification performance. Two feature extraction methods are employed to extract the feature from three different areas of EEG. One is power spectral density（PSD）, and the other is common spatial patterns（CSP）. Classifiers are designed based on the well-known linear discrimination analysis（LDA）. The fusion of the individual classifiers is realized by means of the Choquet fuzzy integral. It is demonstrated that the proposed method comes with better performance compared with the individual classifier.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
Agricultural applications of remote sensing data typically require high spatial resolution and frequent observations.The increasing availability of high spatial resolution imagery meets the spatial resolution requirem...Agricultural applications of remote sensing data typically require high spatial resolution and frequent observations.The increasing availability of high spatial resolution imagery meets the spatial resolution requirement well.However,the long revisit period and frequent cloud contamination severely compromise their ability to monitor crop growth,which is characterized by high temporal heterogeneity.Many spatiotemporal fusion methods have been developed to produce synthetic images with high spatial and temporal resolutions.However,these existing methods focus on fusing low and medium spatial resolution satellite data in terms of model development and validation.When it comes to fusing medium and high spatial resolution images,the applicability remains unknown and may face various challenges.To address this issue,we propose a novel spatiotemporal fusion method,the dual-stream spatiotemporal decoupling fusion architecture model,to fully realize the prediction of high spatial resolution images.Compared with other fusion methods,the model has distinct advantages:(a)It maintains high fusion accuracy and good spatial detail by combining deep-learning-based super-resolution method and partial least squares regression model through edge and color-based weighting loss function;and(b)it demonstrates improved transferability over time by introducing image gradient maps and partial least squares regression model.We tested the StarFusion model at 3 experimental sites and compared it with 4 traditional methods:STARFM(spatial and temporal adaptive reflectance fusion),FSDAF(flexible spatiotemporal data fusion),Fit-FC(regression model fitting,spatial filtering,and residual compensation),FIRST(fusion incorporating spectral autocorrelation),and a deep learning base method-super-resolution generative adversarial network.In addition,we also investigated the possibility of our method to use multiple pairs of coarse and fine images in the training process.The results show that multiple pairs of images provide better overall performance but both of them are better than other comparison methods.Considering the difficulty in obtaining multiple cloud-free image pairs in practice,our method is recommended to provide high-quality Gaofen-1 data with improved temporal resolution in most cases since the performance degradation of single pair is not significant.展开更多
Mapping informal settlements is crucial for resource and utility management and planning.In 2003,the UN-Habitat developed a process for mapping and monitoring urban inequality to support reporting against the sustaina...Mapping informal settlements is crucial for resource and utility management and planning.In 2003,the UN-Habitat developed a process for mapping and monitoring urban inequality to support reporting against the sustainable development goals(SDGs).Informal settlement indicators are used as a framework to carry out image analysis,and include vegetation extent,lacunarity of housing structures/vacant land,road segment type and materials,texture measures of built-up areas,roofing extent of built-up areas and dwelling size.Objectbased image analysis(OBIA)methods are recommended to identify informal settlements.This paper documents the application of OBIA to map informal settlements,drawing on the ontology of Kohli et al.(2012)and the indicators of Owen and Wong(2013)for a Middle Eastern city.Three informal settlements with different land use histories were selected to represent old and new informal settlements in the city of Jeddah,Saudi Arabia.Vegetation extent was the most successful indicator detected,with 100% producer accuracy and over 84% user accuracy,followed by the road network,with 84% producer and user accuracies in older informal settlements and 73% producer accuracy and 96% user accuracy across all case studies.Lacunarity of housing structures/vacant land was detected well in informal settlements.The texture measure indicator was detected using GLCM_(Ent)(R)with low producer accuracy across all case studies.The roofing extent of the built-up area is detected with better producer and user accuracies than texture measures.The dwellings size indicator generally failed to distinguish formal from informal settlements.Informal and formal were distinguished with an overall accuracy of 83%.This research concludes that OBIA is a useful method to map informal settlement indicators in Middle Eastern cities.However,a generic ruleset for mapping informal settlements remains elusive,and each indicator requires significant localised‘tuning’.展开更多
文摘Due to the volume conduction,electroencephalogram（EEG） gives a rather blurred image of brain activities. It is a challenge for generating satisfactory performance with EEG. This paper studies the multiple areas fusion of EEG classifiers to improve the motor imagery EEG classification performance. Two feature extraction methods are employed to extract the feature from three different areas of EEG. One is power spectral density（PSD）, and the other is common spatial patterns（CSP）. Classifiers are designed based on the well-known linear discrimination analysis（LDA）. The fusion of the individual classifiers is realized by means of the Choquet fuzzy integral. It is demonstrated that the proposed method comes with better performance compared with the individual classifier.
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supported by High-Resolution Earth Observation System(09-Y30F01-9001-20/22).
文摘Agricultural applications of remote sensing data typically require high spatial resolution and frequent observations.The increasing availability of high spatial resolution imagery meets the spatial resolution requirement well.However,the long revisit period and frequent cloud contamination severely compromise their ability to monitor crop growth,which is characterized by high temporal heterogeneity.Many spatiotemporal fusion methods have been developed to produce synthetic images with high spatial and temporal resolutions.However,these existing methods focus on fusing low and medium spatial resolution satellite data in terms of model development and validation.When it comes to fusing medium and high spatial resolution images,the applicability remains unknown and may face various challenges.To address this issue,we propose a novel spatiotemporal fusion method,the dual-stream spatiotemporal decoupling fusion architecture model,to fully realize the prediction of high spatial resolution images.Compared with other fusion methods,the model has distinct advantages:(a)It maintains high fusion accuracy and good spatial detail by combining deep-learning-based super-resolution method and partial least squares regression model through edge and color-based weighting loss function;and(b)it demonstrates improved transferability over time by introducing image gradient maps and partial least squares regression model.We tested the StarFusion model at 3 experimental sites and compared it with 4 traditional methods:STARFM(spatial and temporal adaptive reflectance fusion),FSDAF(flexible spatiotemporal data fusion),Fit-FC(regression model fitting,spatial filtering,and residual compensation),FIRST(fusion incorporating spectral autocorrelation),and a deep learning base method-super-resolution generative adversarial network.In addition,we also investigated the possibility of our method to use multiple pairs of coarse and fine images in the training process.The results show that multiple pairs of images provide better overall performance but both of them are better than other comparison methods.Considering the difficulty in obtaining multiple cloud-free image pairs in practice,our method is recommended to provide high-quality Gaofen-1 data with improved temporal resolution in most cases since the performance degradation of single pair is not significant.
文摘Mapping informal settlements is crucial for resource and utility management and planning.In 2003,the UN-Habitat developed a process for mapping and monitoring urban inequality to support reporting against the sustainable development goals(SDGs).Informal settlement indicators are used as a framework to carry out image analysis,and include vegetation extent,lacunarity of housing structures/vacant land,road segment type and materials,texture measures of built-up areas,roofing extent of built-up areas and dwelling size.Objectbased image analysis(OBIA)methods are recommended to identify informal settlements.This paper documents the application of OBIA to map informal settlements,drawing on the ontology of Kohli et al.(2012)and the indicators of Owen and Wong(2013)for a Middle Eastern city.Three informal settlements with different land use histories were selected to represent old and new informal settlements in the city of Jeddah,Saudi Arabia.Vegetation extent was the most successful indicator detected,with 100% producer accuracy and over 84% user accuracy,followed by the road network,with 84% producer and user accuracies in older informal settlements and 73% producer accuracy and 96% user accuracy across all case studies.Lacunarity of housing structures/vacant land was detected well in informal settlements.The texture measure indicator was detected using GLCM_(Ent)(R)with low producer accuracy across all case studies.The roofing extent of the built-up area is detected with better producer and user accuracies than texture measures.The dwellings size indicator generally failed to distinguish formal from informal settlements.Informal and formal were distinguished with an overall accuracy of 83%.This research concludes that OBIA is a useful method to map informal settlement indicators in Middle Eastern cities.However,a generic ruleset for mapping informal settlements remains elusive,and each indicator requires significant localised‘tuning’.
