With the development of artificial intelligence,remote sensing scene interpretation task has attracted extensive attention,which mainly includes scene classification,target detection,hyperspectral classification,and m...With the development of artificial intelligence,remote sensing scene interpretation task has attracted extensive attention,which mainly includes scene classification,target detection,hyperspectral classification,and multi‐modal analysis.The remote sensing scene interpretation has effectively promoted the development of the Earth observation field.It was the intention for this Special Issue to serve as a platform for the publication of the most recent research concepts from remote sensing image.展开更多
Abnormal event detection aims to automatically identify unusual events that do not comply with expectation.Recently,many methods have been proposed to obtain the temporal locations of abnormal events under various det...Abnormal event detection aims to automatically identify unusual events that do not comply with expectation.Recently,many methods have been proposed to obtain the temporal locations of abnormal events under various determined thresholds.However,the specific categories of abnormal events are mostly neglect,which are important to help in monitoring agents to make decisions.In this study,a Temporal Attention Network(TANet)is proposed to capture both the specific categories and temporal locations of abnormal events in a weakly supervised manner.The TANet learns the anomaly score and specific category for each video segment with only video-level abnormal event labels.An event recognition module is exploited to predict the event scores for each video segment while a temporal attention module is proposed to learn a temporal attention value.Finally,to learn anomaly scores and specific categories,three constraints are considered:event category constraint,event separation constraint and temporal smoothness constraint.Experiments on the University of Central Florida Crime dataset demonstrate the effectiveness of the proposed method.展开更多
文摘With the development of artificial intelligence,remote sensing scene interpretation task has attracted extensive attention,which mainly includes scene classification,target detection,hyperspectral classification,and multi‐modal analysis.The remote sensing scene interpretation has effectively promoted the development of the Earth observation field.It was the intention for this Special Issue to serve as a platform for the publication of the most recent research concepts from remote sensing image.
基金supported in part by the National Science Fund for Distinguished Young Scholars under grant no.61925112,in part by the National Natural Science Foundation of China under grant no.61806193 and grant no.61772510Support Program of Shaanxi under grant no.2020KJXX‐091in part by the Key Research Program of Frontier Sciences,Chinese Academy of Sciences under grant no.QYZDY‐SSW‐JSC044.
文摘Abnormal event detection aims to automatically identify unusual events that do not comply with expectation.Recently,many methods have been proposed to obtain the temporal locations of abnormal events under various determined thresholds.However,the specific categories of abnormal events are mostly neglect,which are important to help in monitoring agents to make decisions.In this study,a Temporal Attention Network(TANet)is proposed to capture both the specific categories and temporal locations of abnormal events in a weakly supervised manner.The TANet learns the anomaly score and specific category for each video segment with only video-level abnormal event labels.An event recognition module is exploited to predict the event scores for each video segment while a temporal attention module is proposed to learn a temporal attention value.Finally,to learn anomaly scores and specific categories,three constraints are considered:event category constraint,event separation constraint and temporal smoothness constraint.Experiments on the University of Central Florida Crime dataset demonstrate the effectiveness of the proposed method.
