Fine-grained aircraft target detection in remote sensing holds significant research valueand practical applications,particularly in military defense and precision strikes.Given the complex-ity of remote sensing images...Fine-grained aircraft target detection in remote sensing holds significant research valueand practical applications,particularly in military defense and precision strikes.Given the complex-ity of remote sensing images,where targets are often small and similar within categories,detectingthese fine-grained targets is challenging.To address this,we constructed a fine-grained dataset ofremotely sensed airplanes;for the problems of remote sensing fine-grained targets with obvious head-to-tail distributions and large variations in target sizes,we proposed the DWDet fine-grained tar-get detection and recognition algorithm.First,for the problem of unbalanced category distribution,we adopt an adaptive sampling strategy.In addition,we construct a deformable convolutional blockand improve the decoupling head structure to improve the detection effect of the model ondeformed targets.Then,we design a localization loss function,which is used to improve the model’slocalization ability for targets of different scales.The experimental results show that our algorithmimproves the overall accuracy of the model by 4.1%compared to the baseline model,and improvesthe detection accuracy of small targets by 12.2%.The ablation and comparison experiments alsoprove the effectiveness of our algorithm.展开更多
Due to limitations to extract invariant features for recognition when the aircraft presents various poses and lacks enough samples for training, a novel algorithm called Weighted Marginal Fisher Analysis with Spatiall...Due to limitations to extract invariant features for recognition when the aircraft presents various poses and lacks enough samples for training, a novel algorithm called Weighted Marginal Fisher Analysis with Spatially Smooth （WMFA-SS） for extracting invariant features in aircraft rec- ognition is proposed. According to the Graph Embedding （GE） framework, Heat Kernel function is firstly introduced to characterize the interclass separability when choosing the weights of penalty graph. Furthermore, Laplacian penalty is applied to constraining the coefficients to be spatially smooth in this algorithm. Laplacian penalty is able to incorporate the prior information that neigh- boring pixels are correlated. Besides, using a Laplacian penalty can also avoid the singularity of Laplacian matrix of intrinsic graph. Once compact representations of the images are obtained, it can be considered as invariant features and then be performed in classification to recognize different patterns of aircraft. Real aircraft recognition experiments show the superiority of our proposed WMFA-SS in comparison to other GE algorithms and the current aircraft recognition algorithm; the accuracy rate of our proposed method is 90.00% for dataset BH-AIR1.0 and 99.25% for dataset BH-AIR2.0.展开更多
利用常规天气资料、NCEP/NCAR的1°×1°再分析资料对2012年3月19日发生在北疆沿天山地区强降水过程造成航空器严重积冰的天气进行了分析。结果表明：（1）500 h Pa西南急流和400 h Pa以上偏西急流的存在,配合中低空冷空气...利用常规天气资料、NCEP/NCAR的1°×1°再分析资料对2012年3月19日发生在北疆沿天山地区强降水过程造成航空器严重积冰的天气进行了分析。结果表明：（1）500 h Pa西南急流和400 h Pa以上偏西急流的存在,配合中低空冷空气的补充,为积冰天气的形成提供了适宜的条件。（2）乌鲁木齐上空高湿层随时间逐渐下降,对应的温度、温度露点差向适宜积冰的区域变化,为强积冰天气提供了有利的温湿条件。（3）强积冰区存在一个弱的、持续性的垂直上升运动,垂直运动导致液态水聚集带形成,弱上升运动区对应发生强积冰合适的温湿高度层。（4）19日14时~20日02时,石河子到乌鲁木齐区域积冰指数是一个迅速增大的过程,强积冰区由石河子经呼图壁向乌鲁木齐蔓延,积冰高度随时间下降。展开更多
基金supported by National Natural Science Foundation of China(No.62471034)Hebei Natural Science Foundation(No.F2023105001).
文摘Fine-grained aircraft target detection in remote sensing holds significant research valueand practical applications,particularly in military defense and precision strikes.Given the complex-ity of remote sensing images,where targets are often small and similar within categories,detectingthese fine-grained targets is challenging.To address this,we constructed a fine-grained dataset ofremotely sensed airplanes;for the problems of remote sensing fine-grained targets with obvious head-to-tail distributions and large variations in target sizes,we proposed the DWDet fine-grained tar-get detection and recognition algorithm.First,for the problem of unbalanced category distribution,we adopt an adaptive sampling strategy.In addition,we construct a deformable convolutional blockand improve the decoupling head structure to improve the detection effect of the model ondeformed targets.Then,we design a localization loss function,which is used to improve the model’slocalization ability for targets of different scales.The experimental results show that our algorithmimproves the overall accuracy of the model by 4.1%compared to the baseline model,and improvesthe detection accuracy of small targets by 12.2%.The ablation and comparison experiments alsoprove the effectiveness of our algorithm.
基金co-supported by the National Key Scientific Instrument and Equipment Development Project (No.2012YQ140032)
文摘Due to limitations to extract invariant features for recognition when the aircraft presents various poses and lacks enough samples for training, a novel algorithm called Weighted Marginal Fisher Analysis with Spatially Smooth （WMFA-SS） for extracting invariant features in aircraft rec- ognition is proposed. According to the Graph Embedding （GE） framework, Heat Kernel function is firstly introduced to characterize the interclass separability when choosing the weights of penalty graph. Furthermore, Laplacian penalty is applied to constraining the coefficients to be spatially smooth in this algorithm. Laplacian penalty is able to incorporate the prior information that neigh- boring pixels are correlated. Besides, using a Laplacian penalty can also avoid the singularity of Laplacian matrix of intrinsic graph. Once compact representations of the images are obtained, it can be considered as invariant features and then be performed in classification to recognize different patterns of aircraft. Real aircraft recognition experiments show the superiority of our proposed WMFA-SS in comparison to other GE algorithms and the current aircraft recognition algorithm; the accuracy rate of our proposed method is 90.00% for dataset BH-AIR1.0 and 99.25% for dataset BH-AIR2.0.
文摘利用常规天气资料、NCEP/NCAR的1°×1°再分析资料对2012年3月19日发生在北疆沿天山地区强降水过程造成航空器严重积冰的天气进行了分析。结果表明：（1）500 h Pa西南急流和400 h Pa以上偏西急流的存在,配合中低空冷空气的补充,为积冰天气的形成提供了适宜的条件。（2）乌鲁木齐上空高湿层随时间逐渐下降,对应的温度、温度露点差向适宜积冰的区域变化,为强积冰天气提供了有利的温湿条件。（3）强积冰区存在一个弱的、持续性的垂直上升运动,垂直运动导致液态水聚集带形成,弱上升运动区对应发生强积冰合适的温湿高度层。（4）19日14时~20日02时,石河子到乌鲁木齐区域积冰指数是一个迅速增大的过程,强积冰区由石河子经呼图壁向乌鲁木齐蔓延,积冰高度随时间下降。
