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Multi-Temporal Images Generation for Building Change Detection Performance Promotion

Multi-Temporal Images Generation for Building Change Detecti...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yute Li Wei Li Nan Wang Chenzhong Gao Yin Zhuang He Chen Beijing Key Laboratory of Fractional Signals and Systems Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The changes in building are important basis for urban monitoring. However, due to the rarity and sparsity of the occurrence of changes in buildings, collecting effective bitemporal image pairs is challenging, as it requires long-term observation over several months or even years. Additionally, annotating large-scale change detection datasets is time-consuming and labor-intensive. Consequently, data scarcity issues lead to insufficient training of building change detection models. To address this, we propose a data generation method Building Generation GAN (BG-GAN). Different from other GANs, the BG-GAN is trained based on adversarial consistency loss, enabling the model to generate new bi-temporal image pairs with various types of building changes. To verify the effectiveness of the proposed methods on change detection task, BG-GAN is utilized to perform building change samples generation on two building change detection datasets (LEVIR-CD and WHU-CD). The experimental results demonstrate that the proposed method can improve the robustness and generalization of change detection model to detect pseudo changes.

关键词： Training Accuracy Image synthesis Buildings Geoscience and remote sensing Generative adversarial networks Data models

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HRRP SCATTERING CENTER ESTIMATION BASED ON MODELLED NEURAL NETWORK

HRRP SCATTERING CENTER ESTIMATION BASED ON MODELLED NEURAL N...

引用

IET International Radar Conference 2023, IRC 2023

作者： Zhang, Yu-Ang Zhou, Na Wang, Yanhua Zhang, Liang School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Electromagnetic Sensing Research Center of CEMEE State Key Laboratory Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Advanced Technology Research Institute Beijing Institute of Technology Shandong250300 China

ISBN: (纸本)9781839539954

Scattering center estimation of HRRP is crucial for radar automatic target recognition (RATR). Traditional estimation algorithms either necessitate prior information of the signal or suffer from mismatch issues or possess excessive computational costs. In this paper, we propose an HRRP-modelled neural network (HNN), which addresses the mismatch problem and exhibits better accuracy. HNN combines the HRRP signal model with its layered structure, including input, output and activation function. It is initialized via orthogonal matching pursuit and optimized using back propagation algorithm with dual learning rate. The estimated position and amplitude can be obtained by the weights between layers. Through simulations and experiments, we demonstrate the superiority of HNN over traditional methods. © The Institution of Engineering & technology 2023.

关键词： Backpropagation

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A ROCKFALL MOTION PARAMETER ESTIMATION METHOD BASED ON TDM-MIMO RADAR WITH COMBINED DOUBLE/MULTI-PULSE CHANNELS

A ROCKFALL MOTION PARAMETER ESTIMATION METHOD BASED ON TDM-M...

引用

IET International Radar Conference 2023, IRC 2023

作者： Ren, Xiaoyu Qi, Jingshuang Deng, Yunkai Lin, Ruiqi Chen, Youwang Tian, Weiming 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 Chongqing Innovation Center Beijing Institute of Technology Chongqing401120 China Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China

ISBN: (纸本)9781839539954

MIMO(Multiple-Input Multiple-Output) radar can be effectively applied to the real-time detection of rockfalls, but it is challenging to balance the measurement range of unfuzzy velocity and the real-time detection performance when using TDM(time Division Multiplexing) waveform. Besides, the estimation accuracy of DOA(Direction-Of-Arrival) of rockfalls is easily disturbed by the motion phase. Aiming at solving the above problems, a rockfall motion parameter estimation method based on TDM-MIMO radar with combined double/multi-pulse channels is proposed in this paper. Firstly, by designing the time sequence of TDM signals, the problem of mutual limitation between the detection range of velocity and real-time detection performance is solved. Secondly, the problem of inaccurate angle estimation caused by Doppler frequency is solved. When there is only one target in the range unit, the motion phase should be compensated first. When multiple targets are located in the same range unit, the velocity of these targets is used to judge the corresponding azimuth, and then velocity-azimuth decoupling is completed. Finally, the effectiveness of the proposed method is verified by simulation experiments. © The Institution of Engineering & technology 2023.

关键词： Rock bursts

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DUAL BAND HRRP SCATTERING CENTER ESTIMATION VIA ATOMIC NORM MINIMIZATION

DUAL BAND HRRP SCATTERING CENTER ESTIMATION VIA ATOMIC NORM ...

引用

IET International Radar Conference 2023, IRC 2023

作者： Zhou, Na Zhang, Yu-Ang Wang, Yanhua Zhang, Liang School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Electromagnetic Sensing Research Center of CEMEE State Key Laboratory Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Advanced Technology Research Institute Beijing Institute of Technology Shandong250300 China

ISBN: (纸本)9781839539954

High resolution range profile (HRRP) obtained through air-to-ground radar provides information on the target structure, which is widely used in military and aviation fields. On the other side, dual band radar is able to improve the performance of target detection and recognition by fusing the information of different band. In this paper, we propose a dual band HRRP scattering center estimation method based on atomic norm minimization (ANM). Due to the fact that dual band information reflects the characteristics of the same target, it is modelled as the multiple measurement vector (MMV) and incorporated into the modelling process to effectively utilize the correlation of dual band information and sparse reconstruction. This method estimates in a continuous domain, avoiding the problem of base mismatch. According to the experimental results, this method exhibits outstanding advantages in accuracy and robustness. © The Institution of Engineering & technology 2023.

关键词： Radar target recognition

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Radar range Extended target detection using Prototypical Network 2

Radar range Extended target detection using Prototypical Net...

引用

2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Ma, Wenhao Yuan, Mingchen Wang, Xinyang Zhang, Liang Wang, Yanhua Radar Technology Research Institute Beijing Institute of Technology Beijing100081 China Zhengzhou Research Institute Beijing Institute of Technology Zhengzhou450000 China Electromagnetic Sensing Research Center of Cemee State Key Laboratory Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information: Processing Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China

ISBN: (纸本)9798331515669

In radar ground stationary or slow-moving target detection, the target detection algorithm based on High-Resolution Range Profile (HRRP) is an important technical approach. Traditional target detection algorithms based on HRRP are difficult to apply in target detection tasks under multiple terrains. To this end, this paper proposes a feature detection algorithm based on deep learning for target polarized HRRP. This paper studies an end-to-end target detection algorithm based on prototypical networks. This algorithm utilizes prototypical networks to extract deep features from input HRRP and constructs the feature distribution of clutter. Then, it calculates the distance between the features of the input test sample and the clutter feature center, and compares it with the threshold calculated based on the false alarm probability to complete the detection. Under the backgrounds of bare soil and highway, the algorithm improves by 22% compared to the traditional energy detection algorithm for two types of vehicle targets at a signal-to-clutter ratio (SCR) of 0dB. © 2024 IEEE.

关键词： Clutter (information theory)

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Real-time Implementation of Phased Array Radar Tracking Algorithm Based on embedded GPU 2

Real-time Implementation of Phased Array Radar Tracking Algo...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Fang, Delin Lin, Ruiqi Chang, Ningyuan Chen, Youwang Tian, Weiming Deng, Yunkai Beijing Institute of Technology Radar Technology Research Institute Beijing China Beijing Key Laboratory of Embedded Realtime Information Processing Technology Radar Technology Beijing China Beijing Institute of Technology School of Information and Electronic Beijing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China Beijing Institute of Technology Advanced Technology Research Institute Jinan China

ISBN: (纸本)9798331515669

The embedded Graphics processing Unit (GPU) module, which includes both Central processing Unit (CPU) and GPU processors, can be easily integrated into radar systems, offering high performance and flexibility. Phased array radar tracking algorithm involves numerous modules with poor parallelism between them, leading to significant data blocking issues during sequential execution. Traditional implementations using CPUs or DSPs are time-consuming and inefficient. To address this problem, this paper proposes a real-time implementation solution based on embedded GPU. The solution decouples logic-intensive and computation-intensive tasks in the algorithm and maps them separately to the CPU and GPU within the embedded GPU module. Then three-level hybrid parallel processing method is adopted, which is based on grid-stride-based intra-kernel thread parallelism, CUDA stream asynchronous parallelism and CPU thread parallelism in order to realize real-time processing of radar echo data. Verification experiment using real measured data indicates that the proposed real-time implementation solution achieves significant acceleration with no loss of accuracy on the Jetson AGX Xavier embedded GPU platform, markedly improving tracking efficiency. © 2024 IEEE.

关键词： Digital storage

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Lightweight CNN for HRRP Recognition Based on Attention Mechanism Structured Pruning

Lightweight CNN for HRRP Recognition Based on Attention Mech...

引用

2023 IEEE International Radar Conference, RADAR 2023

作者： Wang, Yanhua Zhang, Zhilong Yuan, Mingchen Liao, Jiandong Zhang, Liang Beijing Institute of Technology Electromagnetic Sensing Research Center of Cemee State Key Lab Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Beijing Institute of Technology Advanced Technology Research Institute Shandong250300 China

ISBN: (纸本)9781665482783

Deep convolutional neural network (CNN) has been widely investigated for radar target high resolution range profile (HRRP) recognition. However, the deep structures of CNN require high storage and computational capabilities, thus restricting its applications with limited resources. In this paper, we design a lightweight CNN model with structure pruning based on the channel-wise attention mechanism. Specifically, the attention value is used to represent the importance of the filters in CNN. Furthermore, Greedy strategy and fine-tuning are adopted in the pruning process to minimize the loss of model performance. The results of public dataset show that the recognition accuracy of the proposed method decreases by less than 0.13% when the pruning ratio is 80%. © 2023 IEEE.

关键词： Convolutional neural networks

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Improved Clutter Suppression Method for Bridge Vibration Measurement Based on GB-MIMO Radar 2

Improved Clutter Suppression Method for Bridge Vibration Mea...

引用

2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Gao, Jiake Wang, Jian Wang, Siqi Qu, Xinran Li, Wenyu Deng, Yunkai Radar Technology Research Institute Bejing Institute of Technology Beijing China Beijing Building Research Institute Corporation Limited Cscec Beijing China Beijing Key Laboratory of Embedded Realtime Information Processing Technology Radar Technology Beijing China Beijing Institute of Technology Chongqing Innovation Center Chongqing China

ISBN: (纸本)9798331515669

Ground based multi-input multi-output (GB-MIMO) radar has been gradually applied in the field of bridge vibration monitoring. During measurement, the interference of static clutter on the vibrating target signal in the same azimuth and range resolution severely affects the vibration measurement. The traditional method of removing static clutter is to estimate static clutter by fitting a circular curve to a complex signal point set, that is, fitting the vector of the circular curve center deviating from the origin of the polar coordinate system. Due to the clustered distribution of vibration points during non vibration time, the trajectory of complex sequence point sets will be distorted, resulting in poor clutter suppression effect. This article proposes an improved method for measuring the dynamic deflection of bridges by suppressing clutter. Firstly, obtain the vibration time period of the vibration candidate points through time-frequency analysis. Secondly, extract the complex sequence of each pixel during the vibration time period, and use the circular trace fitting method to estimate the stationary clutter. Then, based on outlier degree and outlier rate evaluation indicators, select vibration measurement pixels that can achieve effective clutter suppression. The effectiveness of this method was preliminarily verified by comparing the dynamic deflection curves of millimeter wave radar and laser vibrometer through model bridge experiments. In addition, the method was also validated through a high-speed railway bridge experiment. © 2024 IEEE.

关键词： Vibration measurement

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Classification of Bird and Drone Based on Radar time-Frequency Analysis

Classification of Bird and Drone Based on Radar Time-Frequen...

引用

IET International Radar Conference 2023, IRC 2023

作者： Li, Jialin Li, Weidong Wang, Rui Liu, Haibo Wang, Lianjun Zhang, TianRan Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Advanced Technology Research Institute Beijing Institute of Technology Shandong Jinan250300 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Ministry of Education Beijing100081 China Beijing Institute of Electronic System Engineering Beijing100854 China

ISBN: (纸本)9781839539954

To address the classification of 'Low, Small, Slow' targets such as birds and drones, this paper proposes a classification algorithm based on target time-frequency spectrogram analysis. Firstly, radar-collected data is subjected to Short-time Fourier Transform (STFT) to obtain target time-frequency spectrograms, followed by data preprocessing. Subsequently, these time-frequency spectrograms are fed into the designed neural network Residual Pyramid pooling Net (RPNet) for the classification of the two target classes. Validation results based on radar experimental data demonstrate that this algorithm can effectively classify birds and drones, thereby improving the classification performance of small targets. © The Institution of Engineering & technology 2023.

关键词： Drones

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A Biological Echo Identification Method with Single Polarized Weather Radar Using Super-Pixel Segmentation

A Biological Echo Identification Method with Single Polarize...

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IET International Radar Conference 2023, IRC 2023

作者： Yan, Zujing Cui, Kai Zhang, Jingmin Wang, Rui Hu, Cheng Liu, Jiaxing Zhang, Duo Sun, Zhuoran Mao, Huafeng Radar Research Laboratory School of Information and Electronics Beijing Institute of Technology Beijing100081 China No. 208 Research Institute of China Ordnance Industries Beijing100081 China Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Radar Research Laboratory School of Information and Electronic Beijing Institute of Technology Beijing100081 China School of Computer Sciences Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781839539954

Weather radar networks, given their advantages of continental coverage and year-round operation, show great potential in the study of large-scale aerial biological migration. The precise identification and extraction of biological echoes from vast amounts of weather data serve as the essential metrics for assessing the biological monitoring capability of weather radar systems. However, complex weather conditions pose an obstacle to extracting biological information accurately. This study introduces super-pixel segmentation approach in the domain of weather radar-based biological recognition. A cluster-based method is proposed to extract biological information from localized spatial weather radar rendered images. From the perspective of colour space, we confirm the necessity and evident benefits of differentiating them at local spatial level. We collected 750,000 historical records from 108 weather radars nationwide and handpicked 36,000 representative migration cases for labelling, creating a large-scale trainable dataset for studying aerial ecological migration patterns with weather radars. We apply the proposed method to two typical scenes, and the results demonstrate that the accuracy of the proposed method is above 89.38 % in the whole scenes. © The Institution of Engineering & technology 2023.

关键词： Radar imaging

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