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IET International Radar Conference 2020, IET IRC 2020

作者： Shang, Mingyang Qiu, Xiaolan Han, Bing Yang, Junying Ding, Chibiao Zhong, Lihua Zhou, Xiao Aerospace Information Research Institute Chinese Academic of Sciences Beijing China Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China National Key Laboratory of Microwave Imaging Technology Beijing100190 China

ISBN: (纸本)9781839535635

Azimuth multi-channel (AMC) synthetic aperture radar (SAR) is an effective technology to conquer the minimum antenna area constraint and can provide high-resolution and wide-swath (HRWS) SAR images compared with the conventional strip-map SAR. In order to suppress azimuth ambiguity, AMC SAR will face an important problem: channel imbalances estimation and compensation. At first, this paper theoretically analysis the effects of channel imbalances including amplitude imbalance, phase imbalance and range sampling time imbalance (RSTI) on AMC SAR images, where all these channel imbalances will introduce false targets in azimuth, and RSTI will also make a significant influence on the range of SAR images. And then, in order to validate the above result, some simulations based on the parameters are carried out, and the real image acquired by GF-3 is also described. In addition, the intensity, number, and position of false targets are discussed. Finally, this paper makes some conclusions and points out the problems of these results. © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Synthetic aperture radar

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Topology-based automatic 3D modeling from multiple images

Topology-based automatic 3D modeling from multiple images

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International Conference on Wireless Communications and Signal processing (WCSP)

作者： Long Zhang Xuejin Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei P. R. China

3D reconstruction from multiple-view images has drawn a lot of attentions in computer graphics and computer vision communities. Traditional techniques usually end at discrete 3D point clouds computed from feature correspondence. However, geometric structure remains ambiguous in these unstructured point clouds, especially for man-made objects like buildings, indoor scenes. This paper proposes a new method to automatically reconstruct the main geometric structure of the scene composed of planar faces. First, dense 3D point clouds are reconstructed by applying patch-based multi-view stereo (PMVS) algorithm [1]. Then 3D planar primitives are extracted using a RANSAC-based approach [2]. We present a novel method to analyze the adjacency relations of the planar primitives to estimate the 3D intersection lines on the corresponding faces. Junctions and polygonal faces are computed from the 3D intersection lines along with complementary image information to compose the topology structure. Finally, texture for each face is extracted from the image under the best view. Experimental results demonstrate the feasibility of our system by successfully reconstructing the main structure of a wide range of scenes and constructing a texture-mapped piecewise-planar 3D model from images in multiple views.

关键词： Three-dimensional displays Image reconstruction Solid modeling Computational modeling Junctions Topology Surface reconstruction

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Networked Predictive Control Based On The Round-Trip Delay

Networked Predictive Control Based On The Round-Trip Delay

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第三十四届中国控制会议

作者： Wang Binglin Kang Yu Yin Baoqun Xie Zhen Department of Automation University of Science and Technology of China The State Key Laboratory of Fire Science University of Science and Technology of China and Key Laboratory of Technology in Geo-Satial Information Processing and Application System Chinese Academy of Science School of Electrical Engineering and Automation Hefei University of Technology

ISBN: (纸本)9781467374439

Network predictive control is effectively in dealing with time-delay, data dropouts, packets disorders of Network Control system. This paper studies the stochastic stability of a affine nonlinear system within random time delay. The system controller is consist of a control prediction generator(CPG) at the controller side and a network delay compensator(NDC) at the plant side. Different from the previous literatures that model the random delay of the feedback channel and the forward channel as Markov chain respectively, the model in this paper is based on the round-trip delay, which making the predictive control process more concise but without losing generality. Then, this paper gives the necessary and sufficient conditions for stochastic stability of the closed-loop system. Finally, a numerical example is given to demonstrate the effectiveness of the proposed method.

关键词： Predictive Control,Affine system,Markov Process,Stochastic Stability

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A DERAMP DOPPLER PARAMETER ESTIMATION METHOD FOR SPACEBORNE SLIDING SPOTLIGHT SAR

A DERAMP DOPPLER PARAMETER ESTIMATION METHOD FOR SPACEBORNE ...

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IET International Radar Conference 2023, IRC 2023

作者： Chen, Min Shang, Mingyang Qiu, Xiaolan Wen, Xuejiao Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou215123 China Key Laboratory of Technology in Geo-spatial Information Processing and Application Systems Chinese Academy of Sciences Beijing100190 China Suzhou Aerospace Information Research Institute Suzhou215123 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781839539954

Sliding spotlight mode is a widely used working mode for spaceborne synthetic aperture radar (SAR) systems. It can realize a higher azimuth resolution than the stripmap mode and a wider observation swath than the spotlight mode. For sliding spotlight SAR, due to the higher azimuth resolution, the accuracy of the Doppler parameters is of great importance since it affects the construction of the azimuth matching filters and further impacts the focusing quality of the final image. While most of the existing Doppler estimation methods are not applicable for sliding spotlight mode since they do not consider the azimuth spectrum aliasing problem. In this paper, we conduct a detailed analysis of the variation of azimuth time-frequency relationship during the azimuth preprocessing of the two-step focusing process for spaceborne sliding spotlight SAR. Based on the analysis, we propose a new Doppler parameter estimation method dedicated to azimuth preprocessing. We also present our experimental results of real data which can verify the effectiveness of our proposed method. © The Institution of Engineering & technology 2023.

关键词： Doppler radar

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A Method for Object Occlusion Handling Based on Multilevel Track Queues

A Method for Object Occlusion Handling Based on Multilevel T...

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International Conference on Multimedia and Signal processing (CMSP)

作者： Biao Jin Wenlong Hu Yu Zhang Institute of Electronic Key Laboratory of Technology in Geo-spatial Information Processing and Application System CAS Graduate School Chinese Academy and Sciences Beijing China Institute of Electronic Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy and Sciences Beijing China China Electrical Power Research Institute Beijing China

Multi-object tracking is important in many computer vision applications. The major difficulties are due to inter-object or scene occlusion and data association. In this paper, we present a method to automatically detect and track multiple objects in complicated scenario from a single camera that is stationary. The proposed detecting method can prevent the moving objects updated to the background and can restrain ghosts. The moving objects are modeled fusing the hue and edge features. The system achieves tracking the object using the proposed associated strategy based on multilevel track queues. Experiments show the effectiveness of our approach under different conditions with complete occlusions.

关键词： Tracking Image edge detection Pixel Cameras Videos Computational modeling Algorithm design and analysis

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Sasl: Saliency-adaptive sparsity learning for neural network acceleration

arXiv

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arXiv 2020年

作者： Shi, Jun Xu, Jianfeng Tasaka, Kazuyuki Chen, Zhibo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China KDDI Research Japan

Accelerating the inference speed of CNNs is critical to their deployment in real-world applications. Among all the pruning approaches, those implementing a sparsity learning framework have shown to be effective as they learn and prune the models in an end-to-end data-driven manner. However, these works impose the same sparsity regularization on all filters indiscriminately, which can hardly result in an optimal structuresparse network. In this paper, we propose a Saliency-Adaptive Sparsity Learning (SASL) approach for further optimization. A novel and effective estimation of each filter, i.e., saliency, is designed, which is measured from two aspects: The importance for the prediction performance and the consumed computational resources. During sparsity learning, the regularization strength is adjusted according to the saliency, so our optimized format can better preserve the prediction performance while zeroing out more computation-heavy filters. The calculation for saliency introduces minimum overhead to the training process, which means our SASL is very efficient. During the pruning phase, in order to optimize the proposed data-dependent criterion, a hard sample mining strategy is utilized, which shows higher effectiveness and efficiency. Extensive experiments demonstrate the superior performance of our method. Notably, on ILSVRC-2012 dataset, our approach can reduce 49.7% FLOPs of ResNet-50 with very negligible 0.39% top-1 and 0.05% top-5 accuracy degradation. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Aircraft recognition in high resolution SAR images using saliency map and scattering structure features

Aircraft recognition in high resolution SAR images using sal...

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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Fangzheng Dou Wenhui Diao Xian Sun Siyu Wang Kun Fu Guangluan Xu Key Laboratory of Spatial Information Processing and Application System Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781509033331

Scattering structure features of targets is of great importance for Synthetic Aperture Radar (SAR) image analysis. In this paper, a novel algorithm for aircraft recognition in high resolution apron area of SAR images is proposed. The algorithm combines the strength of gradient saliency map and scattering structure features to improve accuracy and efficiency. Specially, Constant False-Alarm Rate (CFAR) algorithm is carried out to segment images. Then, a new efficient object locating method based on directional local gradient map is proposed to detect aircraft targets. Then, the candidate slices as well as template slices are modeled using Gaussian Mixture Model (GMM), which will be treated as structure features. In the recognition stage, a novel similarity measurement algorithm based on Kullback-Leibler Divergence for GMM models is proposed for classification. We conduct experiments on the dataset with 3.0m resolution and the recognition results demonstrate the accuracy of our proposed method.

关键词： Scattering Synthetic aperture radar Aircraft Feature extraction Atmospheric modeling Target recognition Image recognition

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Offline and Online Optical Flow Enhancement for Deep Video Compression

arXiv

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arXiv 2023年

作者： Tang, Chuanbo Sheng, Xihua Li, Zhuoyuan Zhang, Haotian Li, Li Liu, Dong CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China

Video compression relies heavily on exploiting the temporal redundancy between video frames, which is usually achieved by estimating and using the motion information. The motion information is represented as optical flows in most of the existing deep video compression networks. Indeed, these networks often adopt pre-trained optical flow estimation networks for motion estimation. The optical flows, however, may be less suitable for video compression due to the following two factors. First, the optical flow estimation networks were trained to perform inter-frame prediction as accurately as possible, but the optical flows themselves may cost too many bits to encode. Second, the optical flow estimation networks were trained on synthetic data, and may not generalize well enough to real-world videos. We address the twofold limitations by enhancing the optical flows in two stages: offline and online. In the offline stage, we fine-tune a trained optical flow estimation network with the motion information provided by a traditional (non-deep) video compression scheme, e.g. H.266/VVC, as we believe the motion information of H.266/VVC achieves a better rate-distortion tradeoff. In the online stage, we further optimize the latent features of the optical flows with a gradient descent-based algorithm for the video to be compressed, so as to enhance the adaptivity of the optical flows. We conduct experiments on a state-of-the-art deep video compression scheme, DCVC. Experimental results demonstrate that the proposed offline and online enhancement together achieves on average 12.8% bitrate saving on the tested videos, without increasing the model or computational complexity of the decoder side. Copyright © 2023, The Authors. All rights reserved.

关键词： Image coding

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Image-to-image translation with multi-path consistency regularization

arXiv

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arXiv 2019年

作者： Lin, Jianxin Xia, Yingce Wang, Yijun Qin, Tao Chen, Zhibo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China University of Science and Technology of China Microsoft Research Asia

Image translation across different domains has attracted much attention in both machine learning and computer vision communities. Taking the translation from source domain Ds to target domain Dt as an example, existing algorithms mainly rely on two kinds of loss for training: One is the discrimination loss, which is used to differentiate images generated by the models and natural images;the other is the reconstruction loss, which measures the difference between an original image and the reconstructed version through Ds → Dt → Ds translation. In this work, we introduce a new kind of loss, multi-path consistency loss, which evaluates the differences between direct translation Ds → Dt and indirect translation Ds → Da → Dt with Da as an auxiliary domain, to regularize training. For multi-domain translation (at least, three) which focuses on building translation models between any two domains, at each training iteration, we randomly select three domains, set them respectively as the source, auxiliary and target domains, build the multi-path consistency loss and optimize the network. For two-domain translation, we need to introduce an additional auxiliary domain and construct the multi-path consistency loss. We conduct various experiments to demonstrate the effectiveness of our proposed methods, including face-toface translation, paint-to-photo translation, and deraining/de-noising translation. Copyright © 2019, The Authors. All rights reserved.

关键词： Iterative methods

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A method for position offset correction of ship target on SAR image based on AIS data 5

A method for position offset correction of ship target on SA...

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IET International Radar Conference 2020, IET IRC 2020

作者： Lv, Yini Zhong, Lihua Yang, Junying Qiu, Xiaolan Yuan, XinZhe Han, Bing Ding, Chibiao Aerospace Information Research Institute Chinese Academic of Sciences Beijing China Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China National Satellite Ocean Application of Service Beijing China

ISBN: (纸本)9781839535635

This paper proposes a method for the ship target azimuth offset correction on the Synthetic Aperture Radar (SAR) image using Automatic Identification system (AIS) data. Traditional AIS and SAR image ship matching usually use AIS position, size, and other information. This paper uses a continuous position sequence around the SAR image acquisition time, that is, the complete trajectory, to calculate the slant range between the ship and the space-borne SAR platform. By deriving the slant range, the radial velocity can be obtained. According to the radial velocity, the azimuth offset of the moving ship is estimated to correct the ship's position on the SAR image. © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Synthetic aperture radar

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