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3rd International Computing Imaging Conference, CITA 2023

作者： Wang, Zizheng Qi, Feng Wu, Hongming Xidian University Xi’an710126 China Shenyang Institute of Automation Chinese Academy of Sciences China The Key Laboratory of Liaoning Province in Terahertz Imaging and Sensing Shenyang110169 China The Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China The Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110169 China College of information science anf engineering Northeastern University Liaoning Province Shenyang110819 China

ISBN: (纸本)9781510671409

With the increasing demand for autonomous vehicles, the reliable detection and recognition of objects have become paramount. Millimeter-wave radar systems operating at frequencies such as 94GHz offer several advantages, including high resolution, precise range measurement, and resilience to adverse weather conditions. However, accurately characterizing the RCS of objects at this frequency range is essential to optimize the performance of these radar systems. In this work, we present radar Cross Section (RCS) measurements of various objects commonly encountered on the road such as the metal balls, the bricks. The acquired findings illuminate the radar signal fluctuations demonstrated by different entities at a frequency of 94GHz. This knowledge serves as a foundation for developing robust algorithms and signal processing techniques that enhance the object detection, classification, and tracking capabilities of radar-based autonomous driving systems. Moreover, our research provides a valuable dataset of RCS measurements for different objects at 94GHz, which can serve as a benchmark for future studies in the field of radar system design and testing. The availability of such a dataset facilitates the development and validation of radar system models, the evaluation of sensor fusion approaches, and the comparison of performance across different radar hardware and signal processing algorithms. The results contribute to the advancement of radar-based autonomous driving systems by providing crucial insights into the scattering characteristics of objects at millimeter-wave frequencies. The findings have practical implications for optimizing the design, testing, and performance of radar systems, ultimately enhancing the safety and efficiency of autonomous vehicles in real-world scenarios. © 2023 SPIE.

关键词： Millimeter waves

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EDA: Enhanced Domain-Adversarial Training for Anatomical Landmark Detection 22

EDA: Enhanced Domain-Adversarial Training for Anatomical Lan...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Yang, Fan Zhou, S. Kevin School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei 230026 China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advanced Research University of Science and Technology of China Jiangsu Suzhou 215123 China Key Laboratory of Precision and Intelligent Chemistry Ustc Anhui Hefei 230026 China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology Cas Beijing 100190 China

ISBN: (纸本)9798331520526

Manually annotating anatomical landmarks in medical images requires experienced clinicians and is a labor-intensive process. However, recent AI-assisted methods for landmark detection often rely on the training and test data originating from the same domain. This work introduces a novel unsupervised domain adaptation (UDA) framework aimed at anatomical landmark detection from a medical image, designed to bridge the gap between a source domain with labels and an unlabeled target domain. Specifically, we have developed a new Domain-Adversarial Network that incorporates skip connections to transfer and fuse high-resolution feature maps. Additionally, we proposed Dynamic Gaussian Learning, which allows the model to escape from local error regions. We carry out experiments on landmark detection for both head and chest, and the results demonstrate that our method achieves state-of-the-art performances in each experiment. © 2025 IEEE.

关键词： Anatomical Landmark Detection Unsupervised Domain Adaptation

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Multi-component High-Frequency Vibration Error Estimation and Compensation Approach for Multirotor UAV SAR

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IEEE Sensors Journal 2025年

作者： Han, Jiahao Tang, Shiyang Chen, Zhanye Guo, Ping Jiang, Chenghao Lian, Zhixin Zhang, Linrang Xidian University National Key Laboratory of Radar Signal Processing Xi’an 710071 China Southeast University State Key Laboratory of Millimeter Waves Nanjing 210096 China Southeast University Institute of Electromagnetic Space Nanjing 210096 China Xian University of Science and Technology College of Communication and Information Engineering Xian 710054 China

For multirotor unmanned aerial vehicle (UAV) synthetic aperture radar (SAR), the propeller rotation may lead to multi-component high-frequency vibration error (HFVE), seriously affecting imaging quality. To deal with this practical issue, a multi-component HFVE estimation and compensation approach is presented for multirotor UAV SAR, which can be implemented in three steps, i.e., vibration component extraction, vibration parameter estimation, and vibration error suppression. First, an improved ensemble empirical mode decomposition method is utilized to extract the vibration component of HFVE, ensuring the accuracy of component extraction. Then, based on the optimal solution of the correlation function, the parameters of the extracted vibration component are accurately determined to improve the accuracy of the constructed compensation function. Finally, all the vibration components are compensated in cyclic processing to achieve effective suppression of multi-component HFVE. Real data experimental results show that our approach outperforms the existing approach in terms of average impulse response width (0.21 m versus 0.25 m), peak sidelobe ratio (-25.26 dB versus -19.56 dB), and integration sidelobe ratio (-19.43 dB versus -6.54 dB), which verify the effectiveness of our approach. © 2001-2012 IEEE.

关键词： High-frequency vibration error (HFVE) multirotor unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) vibration component extraction vibration parameter estimation

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EarthGPT-X: Enabling MLLMs to Flexibly and Comprehensively Understand Multi-Source Remote Sensing Imagery

arXiv

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arXiv 2025年

作者： Zhang, Wei Cai, Miaoxin Ning, Yaqian Zhang, Tong Zhuang, Yin Chen, He Li, Jun Mao, Xuerui Advanced Research Institute of Multidisciplinary Sciences Beijing Institute of Technology Beijing100081 China School of Mechatronical Engineering Beijing Institute of Technology Beijing100081 China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing314003 China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Beijing100081 China School of Optics and Photonics Beijing Institute of Technology Beijing100081 China School of Computer Science Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Wuhan430078 China

Recent advances in the visual-language area have developed natural multi-modal large language models (MLLMs) for spatial reasoning through visual prompting. However, due to remote sensing (RS) imagery containing abundant geospatial information that differs from natural images, it is challenging to effectively adapt natural spatial models to the RS domain. Moreover, current RS MLLMs are limited in overly narrow interpretation levels and interaction manner, hindering their applicability in real-world scenarios. To address those challenges, a spatial MLLM named EarthGPT-X is proposed, enabling a comprehensive understanding of multi-source RS imagery, such as optical, synthetic aperture radar (SAR), and infrared. EarthGPT-X offers zoom-in and zoom-out insight, and possesses flexible multi-grained interactive abilities. Moreover, EarthGPT-X unifies two types of critical spatial tasks (i.e., referring and grounding) into a visual prompting framework. To achieve these versatile capabilities, several key strategies are developed. The first is the multi-modal content integration method, which enhances the interplay between images, visual prompts, and text instructions. Subsequently, a cross-domain one-stage fusion training strategy is proposed, utilizing the large language model (LLM) as a unified interface for multi-source multi-task learning. Furthermore, by incorporating a pixel perception module, the referring and grounding tasks are seamlessly unified within a single framework. In addition, the experiments conducted demonstrate the superiority of the proposed EarthGPT-X in multi-grained tasks and its impressive flexibility in multi-modal interaction, revealing significant advancements of MLLM in the RS field. Copyright © 2025, The Authors. All rights reserved.

关键词： Image coding

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Visual Instruction Tuning for Holistic and Regional Remote Sensing Imagery Comprehension

Visual Instruction Tuning for Holistic and Regional Remote S...

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

作者： Wei Zhang Miaoxin Cai Tong Zhang Zhuang Yin Xuerui Mao Advanced Research Institute of Multidisciplinary Sciences School of Mechatronical Engineering National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Yangtze Delta Region Academy of Beijing Institute of Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Recently, the visual instruction multimodal large language models (MLLMs) have been extensively studied in the nature scenario. However, current remote sensing (RS) MLLMs mainly focus on image-level understanding and typically allow interaction only through text instructions, resulting in limited accuracy and efficiency. To address those limitations, a visual instruction model is proposed in this article to extend MLLM’s fine-grained perception ability by incorporating bounding boxes with language instruction, aiming at achieving region-level visual understanding. To achieve this goal, a visual instruction dataset featuring multi-modal box-based region-text pairs is constructed. Furthermore, the visual instructions and images are encoded by the different encoders and subsequently fed into a large language model (LLM) along with text instruction tokens for tuning. Finally, experimental results demonstrate the proposed model’s promising performance in region-level image comprehension.

关键词： Visualization Analytical models Accuracy Large language models Data processing Sensors Tuning Remote sensing

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Research on Dataset Preparation and Registration Methods for Multi-Temporal Airport SAR Images

Research on Dataset Preparation and Registration Methods for...

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

作者： Guanghui Wu Lujiao Liu Jianhao Li Hongxin Pan Hao Shi Beijing Institute of Technology Chongqing Innovation Center Beijing Institute of Technology Chongqing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Synthetic Aperture radar (SAR) offers high-resolution imaging capabilities that are unaffected by weather and lighting conditions, making it ideal for complex environments. Recent advancements in spaceborne SAR technology have provided diverse high-resolution, multi-temporal SAR data, crucial for applications like airport monitoring. Despite these advancements, there is a notable lack of multi-temporal airport SAR datasets for research. This study addresses this gap by presenting a method to create a multi-temporal airport SAR image dataset. Utilizing known latitude and longitude information, the method converts geographic coordinates to image projection coordinates to locate airports in SAR images, followed by image conversion and alignment to construct the dataset. Additionally, this study introduces a registration method named SAR-SURF for multi-temporal SAR images, employing a SURF-based Feature Points Detector and an improved Fast Sample Consensus (FSC) algorithm. The experimental results demonstrate that this method effectively reduces speckle noise, ensures matching accuracy, and minimizes processing time, enabling rapid and precise registration of multi-temporal airport SAR images.

关键词： Accuracy Spaceborne radar Noise Detectors Speckle Airports Feature extraction radar polarimetry Reliability Synthetic aperture radar

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An improved ISAR imaging method under low SNR 14

An improved ISAR imaging method under low SNR

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14th European Conference on Synthetic Aperture radar, EUSAR 2022

作者： Gao, Yongpeng Ding, Zegnag Zhang, Tianyi Liu, Shujiang Guo, Xin Li, Pucheng Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401147 China DFH Satellite Co. Ltd Beijing100094 China

ISBN: (纸本)9783800758234

Current inverse synthetic aperture radar (ISAR) imaging methods cannot perform well when the signal-To-noise ratio (SNR) is low. In this paper, an improved ISAR imaging method based on generalized Radon-Fourier transform (GRFT) and relaxation (RELAX) technique is proposed. Through the proposed method, a well-focused radar image of moving target under low SNR condition can be obtained. Further, the estimation error caused by off-grid points as well as the unnecessary side lobe and main lobe values introduced by GRFT based ISAR imaging can be solved. Simulations are conducted to verify the effectiveness of the proposed method. © 2022 institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Inverse synthetic aperture radar

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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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Sparse View HRRP Recognition Based on Dual-Task of Generation and Recognition Method

Sparse View HRRP Recognition Based on Dual-Task of Generatio...

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

作者： Yuanhao Meng Lue Wang Qiang Zhou Xin Zhang Liang Zhang Yanhua Wang 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 China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

In recent years, high-resolution range profiles (HRRPs) have played a significant role in radar target recognition. The pose sensitivity of HRRPs results in unsatisfactory target recognition capabilities under sparse viewing angles. However, for non-cooperative targets, obtaining full-view HRRPs is often challenging, leading to suboptimal performance in HRRP-based target recognition. To address the issue of target recognition under sparse viewing angles, we propose an end-to-end model DGR for sparse view recognition that integrates combined generation and recognition tasks. The DGR model extracts deep features of HRRPs through an attention mechanism and utilizes these features for both target recognition and the generation of adjacent-view HRRPs, with both tasks mutually reinforcing each other for improved performance in recognition and data generation. Furthermore, by employing domain adaptation methods, the model learns the variability of target HRRPs in the source domain and applies this knowledge to the target domain, enhancing its generalization ability. Experimental results demonstrate that the DGR model achieves optimal recognition and generation capabilities simultaneously.

关键词： Adaptation models Sensitivity Target recognition radar Data collection Feature extraction Generative adversarial networks Data augmentation Data models Data mining

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Open Set HRRP Recognition Using Adaptive Learning with Enclosed Decision Boundaries

Open Set HRRP Recognition Using Adaptive Learning with Enclo...

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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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