Modified refractivity (M) profile is an important parameter to describe the atmospheric refraction environment,as well as a key factor in uniquely evaluating electromagnetic propagation effects.In order to improve the...Modified refractivity (M) profile is an important parameter to describe the atmospheric refraction environment,as well as a key factor in uniquely evaluating electromagnetic propagation effects.In order to improve the model-derived M profile in stable (especially very stable) conditions,three nonlinear similarity functions,namely BH91,CB05,SHEBA07,are introduced in this paper to improve the original Babin_V25 model,and the performances of these modified models are verified based on the hydrometeorological observations from tower platforms,which are finally compared with the original Babin_V25 model and Local_HYQ92 model.Results show that introducing nonlinear similarity functions can significantly improve the model-derived M profile;especially,the newly developed SHEBA07 functions manage to reduce the predicted root mean square (rms) differences of M and M slope (for both 0-5m and 5-40m) by 64.5%,16.6%,and 60.4%,respectively in stable conditions.Unfortunately,this improved method reacts little on the evaporation duct height;in contrast,Local_HYQ92 model is capable of reducing the predicted rms differences of M,M slope (for both 0-5m and 5-40m),and evaporation duct height by 76.7%,40.2%,83.7%,and 58.0% respectively.Finally,a new recommendation is made to apply Local_HYQ92 and Babin_SHEBA07 in very stable conditions considering that M slope is more important than evaporation duct height and absolute M value in uniquely determining electromagnetic propagation effects.展开更多
An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyram...An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.展开更多
基金Key project of the National Natural Science Foundation of China(4083095841005029)the "973" National Basis Research and Development Program of China (2009CB421502)
文摘Modified refractivity (M) profile is an important parameter to describe the atmospheric refraction environment,as well as a key factor in uniquely evaluating electromagnetic propagation effects.In order to improve the model-derived M profile in stable (especially very stable) conditions,three nonlinear similarity functions,namely BH91,CB05,SHEBA07,are introduced in this paper to improve the original Babin_V25 model,and the performances of these modified models are verified based on the hydrometeorological observations from tower platforms,which are finally compared with the original Babin_V25 model and Local_HYQ92 model.Results show that introducing nonlinear similarity functions can significantly improve the model-derived M profile;especially,the newly developed SHEBA07 functions manage to reduce the predicted root mean square (rms) differences of M and M slope (for both 0-5m and 5-40m) by 64.5%,16.6%,and 60.4%,respectively in stable conditions.Unfortunately,this improved method reacts little on the evaporation duct height;in contrast,Local_HYQ92 model is capable of reducing the predicted rms differences of M,M slope (for both 0-5m and 5-40m),and evaporation duct height by 76.7%,40.2%,83.7%,and 58.0% respectively.Finally,a new recommendation is made to apply Local_HYQ92 and Babin_SHEBA07 in very stable conditions considering that M slope is more important than evaporation duct height and absolute M value in uniquely determining electromagnetic propagation effects.
基金supported by the National Natural Science Foundation of China(No.62241109)the Tianjin Science and Technology Commissioner Project(No.20YDTPJC01110)。
文摘An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.
