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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Binhua Yu Ping-An Liu Yichen Liu Hongyu Wang Liang Zhang Yanhua Wang Kun Liu Radar Technology Research Institute Beijing Institute of Technology Beijing China Xi’an Modern Control Technology Research Institute Xi’an China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

High resolution range profiles (HRRP), which contain fine geometric structure information of targets, plays animportant role in the field of Radar automatic target recognition (RATR). In the actual application of RATR, radar may often capture targets that are non-cooperative, making it difficult to establish a comprehensive database, and traditional recognition algorithms are prone to misjudgment when facing unknown targets. Therefore, Open set recognition (OSR) is proposed to simultaneously recognize in-database and out-of-database targets. Considering the pose sensitivity of HRRP, minor changes in the target’s pose can cause significant changes in the HRRP images, leading to a potentially dispersed distribution of samples from the same category in the feature space, which makes it difficult for traditional fixed decision boundaries in OSR to adapt. To address this, this paper proposes an HRRP open set recognition method capable of adaptively learning closed decision boundaries. Based on Hierarchical dirichlet processes (HDP), the method achieves adaptive clustering of subclasses under different categories according to the data distribution characteristics of each category, determining multiple small decision boundaries within each category, and allowing the decision boundaries to dynamically adjust according to the sample features. Finally, experiments with real HRRP data have verified the effectiveness of the proposed method.

关键词： Training Sensitivity Image resolution Image recognition Target recognition Databases Imaging Radar applications Signal resolution Dispersion

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Lightweight CNN for Radar HRRP Recognition Using NAS-Based Pruning and Multi-Knowledge Distillation

Lightweight CNN for Radar HRRP Recognition Using NAS-Based P...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Zhilong Zhang Heke Wu Yinhui Xu Liang Zhang Yanhua Wang Radar Technology Research Institute Beijing Institute of Technology Beijing China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou China Xi'an Modern Control Technology Research Institute Xi'an China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Deep convolutional neural network (CNN) has been widely studied in radar target high resolution range profile (HRRP) recognition. However, the CNN with deep structure requires high storage and computational capabilities, thus restricting its applications with limited resources. In this paper, we propose a differentiable neural architecture search (NAS)-based channel pruning and multi-knowledge distillation (MKD) to prune and fine-tune the CNN for lightweight. In the pruning stage, NAS method is used to automatically find the good-performance pruning structure by optimizing channel preserve scores. After the search process, these scores are used for global pruning to derive the pruned model. In the fine-tuning stage, MKD method combine the physical scattering knowledge with the category knowledge from pretrained model to restore the pruned model performance. Experimental results on VGG-16 with HRRP dataset inversed from the MSTAR show that the recognition accuracy of the proposed method decreases 1.52% by using 5% model parameters.

关键词： Accuracy Target recognition Neural networks Scattering Radar Data processing Data models Neural architecture search Convolutional neural networks

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Multi-scale object detection by top-down and bottom-up feature pyramid network

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Journal of Systems Engineering and Electronics 2019年第1期30卷 1-12页

作者： ZHAO Baojun ZHAO Boya TANG Linbo WANG Wenzheng WU Chen School of Information and Electronics Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology

While moving ahead with the object detection technology, especially deep neural networks, many related tasks, such as medical application and industrial automation, have achieved great success. However, the detection of objects with multiple aspect ratios and scales is still a key problem. This paper proposes a top-down and bottom-up feature pyramid network(TDBU-FPN),which combines multi-scale feature representation and anchor generation at multiple aspect ratios. First, in order to build the multi-scale feature map, this paper puts a number of fully convolutional layers after the backbone. Second, to link neighboring feature maps, top-down and bottom-up flows are adopted to introduce context information via top-down flow and supplement suboriginal information via bottom-up flow. The top-down flow refers to the deconvolution procedure, and the bottom-up flow refers to the pooling procedure. Third, the problem of adapting different object aspect ratios is tackled via many anchor shapes with different aspect ratios on each multi-scale feature map. The proposed method is evaluated on the pattern analysis, statistical modeling and computational learning visual object classes(PASCAL VOC)dataset and reaches an accuracy of 79%, which exhibits a 1.8% improvement with a detection speed of 23 fps.

关键词： convolutional neural network (CNN) feature pyramid network (FPN) object detection deconvolution.

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Consecutive Pretraining: A Knowledge Transfer Learning Strategy with Relevant Unlabeled Data for Remote Sensing Domain

arXiv

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arXiv 2022年

作者： Zhang, Tong Gao, Peng Dong, Hao Zhuang, Yin Wang, Guanqun Zhang, Wei Chen, He Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing100081 China The Shang Hai AI laboratory Shanghai100024 China Peking University Beijing100871 China

Currently, under supervised learning, a model pretrained by a large-scale nature scene dataset and then fine-tuned on a few specific task labeling data is the paradigm that has dominated the knowledge transfer learning. It has reached the status of consensus solution for task-aware model training in remote sensing domain (RSD). Unfortunately, due to different categories of imaging data and stiff challenges of data annotation, there is not a large enough and uniform remote sensing dataset to support large-scale pretraining in RSD. Moreover, pretraining models on large-scale nature scene datasets by supervised learning and then directly fine-tuning on diverse downstream tasks seems to be a crude method, which is easily affected by inevitable labeling noise, severe domain gaps and task-aware discrepancies. Thus, in this paper, considering the self-supervised pretraining and powerful vision transformer (ViT) architecture, a concise and effective knowledge transfer learning strategy called ConSecutive PreTraining (CSPT) is proposed based on the idea of not stopping pretraining in natural language processing (NLP), which can gradually bridge the domain gap and transfer knowledge from the nature scene domain to the RSD. The proposed CSPT also can release the huge potential of unlabeled data for task-aware model training. Finally, extensive experiments are carried out on twelve datasets in RSD involving three types of downstream tasks (e.g., scene classification, object detection and land cover classification) and two types of imaging data (e.g., optical and SAR). The results show that by utilizing the proposed CSPT for task-aware model training, almost all downstream tasks in RSD can outperform the previous method of supervised pretraining-then-fine-tuning and even surpass the state-of-the-art (SOTA) performance without any expensive labeling consumption and careful model design. Copyright © 2022, The Authors. All rights reserved.

关键词： Supervised learning

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Tomographic imaging method of GB-ArcSAR with arc-shaped interferometric baseline

Tomographic imaging method of GB-ArcSAR with arc-shaped inte...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Hanpu Zhou Kai Yan Jian Wang Xiaoxiao Li Weiming Tian Yunkai Deng Radar Technology Research Institute Bejing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing China Beijing Building Research Institute Corporation Limited of CSCEC Beijing China Advanced Technology Research Institute Beijing Institute of Technology Jinan China Chongqing Innovation Center Beijing Institute of Technology China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

When observing building scenarios, there exists the layover problem. Synthetic aperture radar (SAR) tomography can use multiple two-dimensional (2D) SAR images acquired from different heights to inverse the range-azimuth-height 3D information of the targets. Ground-based arc-scanning synthetic aperture radar (GB-ArcSAR) can obtain a series of SAR images at different heights by performing azimuthal scanning on horizontal planes with different pitch angles, which makes it has tomography capabilities. However, the baseline of GB-ArcSAR is arc-shaped, which makes the existing tomographic imaging methods suitable for linear tomographic baselines cannot be directly applied to GB-ArcSAR. This paper proposes a tomographic imaging method of GB-ArcSAR with arc-shaped interferometric baseline. Through Taylor expansion of the distance between the target and the sampling position, a mapping relationship between the target pitch angle and interferometric phase was established. Based on the mapping relationship, the measurement matrix was constructed. Validation of the proposed method is achieved through simulations and measured data.

关键词： Point cloud compression Three-dimensional displays Two-dimensional displays Superresolution Tomography Apertures Radar imaging Taylor series Radar polarimetry Synthetic aperture radar

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An Adaptive Phase Noise Filtering Approach For Multi-Frequency Interferometric SAR

An Adaptive Phase Noise Filtering Approach For Multi-Frequen...

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IEEE International Conference on Radar

作者： Zhen Wang Zegang Ding Yan Wang Xinnong Ma Linghao Li Tao Zeng School of Information and electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-time Information Processing Technology

ISBN: (纸本)9781665468893

Multi-frequency (MF) interferometric SAR (InSAR) provides an effective way for the surveying of steep terrains. Phase noise filtering is a key step in MF-InSAR processing which determines the quality of the interferograms and affects the accuracy of inversed elevation. Different from the traditional filtering method of single frequency (SF) InSAR, an adaptive filtering approach for MF-InSAR based on MF phase fusion is proposed in this paper. The main contributions of this method is summarized into two aspects. First, the local fringe frequency estimation (LFFE) is extended from SF to MF case. Second, the non-linear model is introduced in the adaptive phase filtering in the local window based on MF-LFFE to adapt to complex terrains. The computer simulation and the real dual-frequency airborne experiment validate the proposed approach.

关键词： Phase noise Adaptation models Filtering Computer simulation Computational modeling Atmospheric modeling Estimation

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Coherence Analysis and Interferometric Measurement of P-Band Repeat Pass UAV InSAR

Coherence Analysis and Interferometric Measurement of P-Band...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Xin Xie Yunkai Deng Zihang Jiang Xiaoxiao Li Weiming Tian Radar Technology Research Institute Beijing Institute of Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Beijing Building Research Institute Co. Ltd. of CSCEC Beijing China Advanced Technology Research Institute Beijing Institute of Technology Jinan China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Miniaturized and lightweight unmanned aerial vehicles (UAV) provide a flexible platform for synthetic aperture radar (SAR). The application of UAV interferometric SAR (InSAR) is gradually increasing in interferometric measurement fields. Leveraging the P-band signal and the capability to penetrate leaf clusters, these UAV InSAR systems demonstrate significant potential for topographic mapping and deformation monitoring, even in challenging surface conditions. This paper introduces the primary challenges associated with UAV InSAR, with a specific focus on analyzing the coherence of UAV InSAR. Additionally, we present interferometric measurement results obtained by our self-developed P-band UAV InSAR system and the processing flow we proposed. These results effectively address key challenges and validate the feasibility of the system for repeat pass interferometric measurements.

关键词： Optical interferometry Deformation Geoscience and remote sensing Coherence Gain measurement Autonomous aerial vehicles Surface topography

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A Self-supervised SAR Image Despeckling Strategy Based on Parameter-sharing Convolutional Neural Networks

arXiv

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arXiv 2023年

作者： Chen, Liang Yin, Yifei Shi, Hao Sheng, Qingqing Li, Wei The Radar Research Laboratory Beijing Institute of Technology Beijing10081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401135 China The Beijing Key Laboratory of Embedded Real-time Processing Technology Beijing100081 China The Radar Research Laboratory School of Information and Electronics Beijing Institute of Technology Beijing10081 China The School of Information and Electronics Beijing Institute of Technology Beijing10081 China

Speckle noise is generated due to the SAR imaging mechanism, which brings difficulties in SAR image interpretation. Hence, despeckling is a helpful step in SAR pre-processing. Nowadays, deep learning has been proved to be a progressive method for SAR image despeckling. Most deep learning methods for despeckling are based on supervised learning, which needs original SAR images and speckle-free SAR images to train the network. However, the speckle-free SAR images are generally not available. So, this issue was tackled by adding multiplicative noise to optical images synthetically for simulating speckled image. Therefore, there are following challenges in SAR image despeckling: (1) lack of speckle-free SAR image;(2) difficulty in keeping details such as edges and textures in heterogeneous areas. To address these issues, we propose a self-supervised SAR despeckling strategy that can be trained without speckle-free images. Firstly, the feasibility of SAR image despeckling without speckle-free images is proved theoretically. Then, the sub-sampler based on the adjacent-syntropy criteria is proposed. The training image pairs are generated by the sub-sampler from real-word SAR image to estimate the noise distribution. Furthermore, to make full use of training pairs, the parameter sharing convolutional neural networks are adopted. Finally, according to the characteristics of SAR images, a multi-feature loss function is proposed. The proposed loss function is composed of despeckling term, regular term and perception term, to constrain the gap between the generated paired images. The ability of edge and texture feature preserving is improved simultaneously. Finally, qualitative and quantitative experiments are validated on real-world SAR images, showing better performances than several advanced SAR image despeckling methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Synthetic aperture radar

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Model Switching Detection-Aided Adaptive Transition Probability Matrix IMM Algorithm

Model Switching Detection-Aided Adaptive Transition Probabil...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Youwang Chen Yunkai Deng Weiming Tian Radar Technology Research Institute Beijing Institute of Technology Beijing China Yangtze Delta Region Academy Beijing Institute of Technology Jiaxing Advanced Technology Research Institute Beijing Institute of Technology Jinan China Chongqing Innovation Center Beijing Institute of Technology Chongqing China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing China School of Information and Electronic Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

In conventional interactive multiple model (IMM) algorithm, the fixed transition probability matrix results in slow model switching and low tracking accuracy. Existing adaptive transition probability IMM algorithms still suffer from large errors during model switching. This paper proposes a model switching detection-aided adaptive transition probability matrix IMM algorithm. To deal with model switch time delay, a transition probability correction function is established, capable of dynamically and rapidly adjusting the transition probabilities. And a model switching detection method is proposed to improve tracking accuracy during model switching. By the numerical simulation, the method's efficacy is substantiated and confirmed.

关键词： Adaptation models Accuracy Heuristic algorithms Delay effects Switches Numerical simulation Data processing Numerical models

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An Anti-background Interference Object Detection Method for Complex Remote Sensing Images

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Tang, Wei Zhao, Xidong Zhang, Qingjun Wang, Wenzheng Zhao, Baojun China Academy of Space Technology Shandong Institute of Space Electronic Technology Yantai264003 China Changfeng Electromechanical Technology Research and Design Institute Beijing100086 China Remote Sensing General Department of China Academy of Space Technology Beijing100086 China Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology School of Information and Electronics Beijing100081 China

Object detection in remote sensing images differs from general object detection in that it faces numerous challenges such as false alarm interference. However, conventional object detection methods primarily focus on scale and orientation variations, insufficiently addressing issues such as object blurring and false alarms, which frequently occur in large-scale Earth observation scenarios. To address this issue, this paper proposes an anti-interference object-detection algorithm based on mask guidance and adaptive margin loss, termed MG2ADet. Specifically, we devise a mask guidance module that leverages pseudo-mask labels to guide network features towards focusing on foreground regions, thereby enhancing the discriminability of object features and suppressing background noise interference. Besides, to further differentiate object and background features, we designed an adaptive margin classification loss function that guides the detection model in learning strongly discriminative features. This classification loss adaptively adjusts the angular margin penalty based on the image quality score of samples, assigning varying weights to different difficult samples, and ultimately improving the detection model's ability to discriminate between blurred objects and similar distractors. Extensive experiments on public remote sensing datasets demonstrated the excellent detection performance of our algorithm in comparison with numerous existing detectors (both one-stage and two-stage detectors). © 2008-2012 IEEE.

关键词： Object detection

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