This paper focuses on the general case （GC） airborne bistatic synthetic aperture radar （SAR） data processing, and a new analytical imaging algorithm based on the extended Loffeld＇s bistatic formula （ELBF） is pr...This paper focuses on the general case （GC） airborne bistatic synthetic aperture radar （SAR） data processing, and a new analytical imaging algorithm based on the extended Loffeld＇s bistatic formula （ELBF） is proposed. According to the bistatic SAR geometry, the track decoupling formulas that convert the bistatic geometry to the receiver-referenced geometry in a concise way are derived firstly. Then phase terms of ELBF are decomposed into two independent phase terms as the range phase term and the azimuth phase term in a new way. To get the focusing result, the bistatic deformation （BD） term is compensated in the two-dimensional （2- D） frequency domain, and the space-variances of the range phase term and the azimuth phase term are eliminated by chirp scaling （CS） and chirp z-transform （CZT）, respectively. The effectiveness of the proposed algorithm is verified by the simulation results.展开更多
Since there is lack of methodology to assess the performance of defogging algorithm and the existing assessment methods have some limitations,three new methods for assessing the defogging algorithm were proposed.One w...Since there is lack of methodology to assess the performance of defogging algorithm and the existing assessment methods have some limitations,three new methods for assessing the defogging algorithm were proposed.One was using synthetic foggy image simulated by image degradation model to assess the defogging algorithm in full-reference way.In this method,the absolute difference was computed between the synthetic image with and without fog.The other two were computing the fog density of gray level image or constructing assessment system of color image from human visual perception to assess the defogging algorithm in no-reference way.For these methods,an assessment function was defined to evaluate algorithm performance from the function value.Using the defogging algorithm comparison,the experimental results demonstrate the effectiveness and reliability of the proposed methods.展开更多
Due to the limited scenes that synthetic aperture radar(SAR)satellites can detect,the full-track utilization rate is not high.Because of the computing and storage limitation of one satellite,it is difficult to process...Due to the limited scenes that synthetic aperture radar(SAR)satellites can detect,the full-track utilization rate is not high.Because of the computing and storage limitation of one satellite,it is difficult to process large amounts of data of spaceborne synthetic aperture radars.It is proposed to use a new method of networked satellite data processing for improving the efficiency of data processing.A multi-satellite distributed SAR real-time processing method based on Chirp Scaling(CS)imaging algorithm is studied in this paper,and a distributed data processing system is built with field programmable gate array(FPGA)chips as the kernel.Different from the traditional CS algorithm processing,the system divides data processing into three stages.The computing tasks are reasonably allocated to different data processing units(i.e.,satellites)in each stage.The method effectively saves computing and storage resources of satellites,improves the utilization rate of a single satellite,and shortens the data processing time.Gaofen-3(GF-3)satellite SAR raw data is processed by the system,with the performance of the method verified.展开更多
It well known that vehicle detection is an important component of the field of object detection.However,the environment of vehicle detection is particularly sophisticated in practical processes.It is comparatively dif...It well known that vehicle detection is an important component of the field of object detection.However,the environment of vehicle detection is particularly sophisticated in practical processes.It is comparatively difficult to detect vehicles of various scales in traffic scene images,because the vehicles partially obscured by green belts,roadblocks or other vehicles,as well as influence of some low illumination weather.In this paper,we present a model based on Faster ReCNN with NAS optimization and feature enrichment to realize the effective detection of multi-scale vehicle targets in traffic scenes.First,we proposed a Retinex-based image adaptive correction algorithm(RIAC)to enhance the traffic images in the dataset to reduce the influence of shadow and illumination,and improve the image quality.Second,in order to improve the feature expression of the backbone network,we conducted Neural Architecture Search(NAS)on the backbone network used for feature extraction of Faster ReCNN to generate the optimal cross-layer connection to extract multi-layer features more effectively.Third,we used the object Feature Enrichment that combines the multi-layer feature information and the context information of the last layer after cross-layer connection to enrich the information of vehicle targets,and improve the robustness of the model for challenging targets such as small scale and severe occlusion.In the implementation of the model,K-means clustering algorithm was used to select the suitable anchor size for our dataset to improve the convergence speed of the model.Our model has been trained and tested on the UN-DETRAC dataset,and the obtained results indicate that our method has art-of-state detection performance.展开更多
Bistatic SAR possesses characteristic of the azimuth space-variant when the velocities of transmitter and receiver are not equal. The geometric model of BiSAR with the parallel trajectories and the nonequal platform v...Bistatic SAR possesses characteristic of the azimuth space-variant when the velocities of transmitter and receiver are not equal. The geometric model of BiSAR with the parallel trajectories and the nonequal platform velocities is presented. Analyzing the motion relationship of transmitter and receiver, the formula of azimuth spacevariant is derived in time domain. Via Taylor polynomial expansions, the azimuth space-variant is factorized by four terms： zero-order, first-order, second-order, and third-order term. And, their impacts on impulse response are illuminated. Some characteristics about azimuth space-variant of airborne BiSAR case are exhibited by simulation experiments, and these simulated results are coincident with the formulae of azimuth space-variant.展开更多
A multifeature statistical image segmentation algorithm is described. Multiple features such as grey, edge magnitude and correlation are combined to form a multidimensional space statistics. The statistical algorithm ...A multifeature statistical image segmentation algorithm is described. Multiple features such as grey, edge magnitude and correlation are combined to form a multidimensional space statistics. The statistical algorithm is used to segment an image using the decision curved surface determined by the multidimensional feature function. The segmentation problem which is difficult to solve using the features independently will be readily solved using the same features jointly. An adaptive segmentation algorithm is discussed. Test results of the real-time TV tracker newly developed have shown that the segmentation algorithm discussed here improves effectively the image segmentation quality and system tracking performance.展开更多
文摘This paper focuses on the general case （GC） airborne bistatic synthetic aperture radar （SAR） data processing, and a new analytical imaging algorithm based on the extended Loffeld＇s bistatic formula （ELBF） is proposed. According to the bistatic SAR geometry, the track decoupling formulas that convert the bistatic geometry to the receiver-referenced geometry in a concise way are derived firstly. Then phase terms of ELBF are decomposed into two independent phase terms as the range phase term and the azimuth phase term in a new way. To get the focusing result, the bistatic deformation （BD） term is compensated in the two-dimensional （2- D） frequency domain, and the space-variances of the range phase term and the azimuth phase term are eliminated by chirp scaling （CS） and chirp z-transform （CZT）, respectively. The effectiveness of the proposed algorithm is verified by the simulation results.
基金Projects(91220301,61175064,61273314)supported by the National Natural Science Foundation of ChinaProject(126648)supported by the Postdoctoral Science Foundation of Central South University,ChinaProject(2012170301)supported by the New Teacher Fund for School of Information Science and Engineering,Central South University,China
文摘Since there is lack of methodology to assess the performance of defogging algorithm and the existing assessment methods have some limitations,three new methods for assessing the defogging algorithm were proposed.One was using synthetic foggy image simulated by image degradation model to assess the defogging algorithm in full-reference way.In this method,the absolute difference was computed between the synthetic image with and without fog.The other two were computing the fog density of gray level image or constructing assessment system of color image from human visual perception to assess the defogging algorithm in no-reference way.For these methods,an assessment function was defined to evaluate algorithm performance from the function value.Using the defogging algorithm comparison,the experimental results demonstrate the effectiveness and reliability of the proposed methods.
基金Project(2017YFC1405600)supported by the National Key R&D Program of ChinaProject(18JK05032)supported by the Scientific Research Project of Education Department of Shaanxi Province,China。
文摘Due to the limited scenes that synthetic aperture radar(SAR)satellites can detect,the full-track utilization rate is not high.Because of the computing and storage limitation of one satellite,it is difficult to process large amounts of data of spaceborne synthetic aperture radars.It is proposed to use a new method of networked satellite data processing for improving the efficiency of data processing.A multi-satellite distributed SAR real-time processing method based on Chirp Scaling(CS)imaging algorithm is studied in this paper,and a distributed data processing system is built with field programmable gate array(FPGA)chips as the kernel.Different from the traditional CS algorithm processing,the system divides data processing into three stages.The computing tasks are reasonably allocated to different data processing units(i.e.,satellites)in each stage.The method effectively saves computing and storage resources of satellites,improves the utilization rate of a single satellite,and shortens the data processing time.Gaofen-3(GF-3)satellite SAR raw data is processed by the system,with the performance of the method verified.
基金This research was funded by the National Natural Science Foundation of China(grant number:61671470)the Key Research and Development Program of China(grant number:2016YFC0802900).
文摘It well known that vehicle detection is an important component of the field of object detection.However,the environment of vehicle detection is particularly sophisticated in practical processes.It is comparatively difficult to detect vehicles of various scales in traffic scene images,because the vehicles partially obscured by green belts,roadblocks or other vehicles,as well as influence of some low illumination weather.In this paper,we present a model based on Faster ReCNN with NAS optimization and feature enrichment to realize the effective detection of multi-scale vehicle targets in traffic scenes.First,we proposed a Retinex-based image adaptive correction algorithm(RIAC)to enhance the traffic images in the dataset to reduce the influence of shadow and illumination,and improve the image quality.Second,in order to improve the feature expression of the backbone network,we conducted Neural Architecture Search(NAS)on the backbone network used for feature extraction of Faster ReCNN to generate the optimal cross-layer connection to extract multi-layer features more effectively.Third,we used the object Feature Enrichment that combines the multi-layer feature information and the context information of the last layer after cross-layer connection to enrich the information of vehicle targets,and improve the robustness of the model for challenging targets such as small scale and severe occlusion.In the implementation of the model,K-means clustering algorithm was used to select the suitable anchor size for our dataset to improve the convergence speed of the model.Our model has been trained and tested on the UN-DETRAC dataset,and the obtained results indicate that our method has art-of-state detection performance.
基金Sichuan Provincial Youth Science and Technology Foundation (06ZQ026-006)
文摘Bistatic SAR possesses characteristic of the azimuth space-variant when the velocities of transmitter and receiver are not equal. The geometric model of BiSAR with the parallel trajectories and the nonequal platform velocities is presented. Analyzing the motion relationship of transmitter and receiver, the formula of azimuth spacevariant is derived in time domain. Via Taylor polynomial expansions, the azimuth space-variant is factorized by four terms： zero-order, first-order, second-order, and third-order term. And, their impacts on impulse response are illuminated. Some characteristics about azimuth space-variant of airborne BiSAR case are exhibited by simulation experiments, and these simulated results are coincident with the formulae of azimuth space-variant.
文摘A multifeature statistical image segmentation algorithm is described. Multiple features such as grey, edge magnitude and correlation are combined to form a multidimensional space statistics. The statistical algorithm is used to segment an image using the decision curved surface determined by the multidimensional feature function. The segmentation problem which is difficult to solve using the features independently will be readily solved using the same features jointly. An adaptive segmentation algorithm is discussed. Test results of the real-time TV tracker newly developed have shown that the segmentation algorithm discussed here improves effectively the image segmentation quality and system tracking performance.
