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2nd International Conference on Geographic Information and Remote Sensing technology, GIRST 2023

作者： Wu, Shujie Deng, Jie Ruan, Feng Wang, Wei Fan, Hongqi Guo, Pengcheng Xi’an Electronic Engineering Research Institute Xi’an China National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Changsha China

ISBN: (纸本)9781510668478

In the detection of nearshore ships in polarimetric SAR (PolSAR) images, detecting ship targets becomes challenging when they are densely distributed or located in areas with strong sea clutter scattering. To achieve adaptive and accurate detection of nearshore ship targets in PolSAR images, this paper proposes an adaptive superpixel-level CFAR detection method based on the improved geometric perturbation polarimetric notch filter (GP-PNF). Firstly, the entire image is segmented into superpixels using the polarimetric SLIC method, and sea-land segmentation is performed utilizing the OTSU method. By employing an automatic censoring (AC) mechanism, superpixels containing pure clutter are selected to estimate the parameters. Following this, the improved GP-PNF is utilized to construct the statistical metric for detection. Finally, the detection results are obtained based on a new superpixel-level CFAR strategy and subsequent post-processing. Experimental results based on GF-3 full-polarization SAR data demonstrate that the proposed method is capable of accurately detecting nearshore ship targets in PolSAR images. © 2023 SPIE.

关键词： Synthetic aperture radar

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INSHORE SHIP DETECTION USING HIGH-RESOLUTION SAR IMAGES BASED ON MULTI-FEATURE FUSION

INSHORE SHIP DETECTION USING HIGH-RESOLUTION SAR IMAGES BASE...

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IET International Radar Conference 2023, IRC 2023

作者： Wu, Shujie Wang, Wei Ruan, Feng Zhang, Huiqiang Deng, Jie Guo, Pengcheng Fan, Hongqi Xi'an Electronic Engineering Research Institute Xi'an China National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Changsha China

ISBN: (纸本)9781839539954

The scattering characteristics of inshore ships in SAR images are similar to those of buildings and other man-made targets on land, making it difficult to detect them. In order to accurately detect the inshore ships in high-resolution SAR images, this paper proposes a multi-feature fusion method. Firstly, coarse sea-land segmentation is achieved by OTSU method and ROI (region of interest) is quickly determined by the maximum stability extremal region (MSER) method. After dividing the ROI into grid slices, the features of candidate targets and their surrounding backgrounds in the ROI are computed. And finally, background resolution (BR) is constructed by feature fusion to distinguish false alarm targets from real targets. Experiments based on satellite-based SAR data show that the method proposed in this paper has good detection accuracy and timeliness. © The Institution of Engineering & technology 2023.

关键词： Radar imaging

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GPU acceleration of a fast phase compensation algorithm in inverse synthetic aperture radar 5

GPU acceleration of a fast phase compensation algorithm in i...

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IET International Radar Conference 2020, IET IRC 2020

作者： Lu, Yue Zhang, Yue Yang, Jian Science and Technology on Automatic Target Recognition Laboratory National University of Defense Technology Changsha410073 China School of Electronic Engineering Xidian University Xi'an710071 China

ISBN: (纸本)9781839535635

Phase compensation is an essential part of inverse synthetic aperture radar imaging. Minimum entropy-based phase compensation methods have been widely applied due to their robustness under low signal-to-noise ratio (SNR) situations. However, these methods suffer from huge computation complexity and therefore, can be hardly used in real-time imaging situations. In this paper, a novel real-time phase compensation method combining Doppler centroid tracking (DCT), prominent range bins extraction, and fast minimum entropy phase compensation (FMEPC) is proposed. To achieve high efficiency, the parallel version of the proposed algorithm is implemented on Graphic Processing Unit (GPU). Experiments based on real-measured data show that almost 10 times speed-up can be realized. © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Inverse synthetic aperture radar

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Adaptive Surface Fitting Convolution for 3D Point Cloud Analysis 5

Adaptive Surface Fitting Convolution for 3D Point Cloud Anal...

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5th International Conference on Automation, Control and Robotics Engineering, CACRE 2020

作者： Cao, Hezhi Ma, Yanxin Zhan, Ronghui Ma, Chao Zhang, Jun National University of Defense Technology National Key Laboratory of Science and Technology on Automatic Target Recognition Changsha China National University of Defense Technology College of Meteorology and Oceanography Changsha China

ISBN: (纸本)9781728198880

Traditional Convolutional Neural Networks (CNN) are limited to extract informative local features of point clouds due to the fixed geometric structures in convolution kernel against irregular and unstructured point clouds. It usually requires data transformation such as voxelization or projection, inducing a possible loss of information. Instead of fitting the input points to the kernel by regularization, we choose to fit the kernel to input points to conduct convolution. In this paper, we present a new method to define and compute convolution directly on 3D point clouds by Adaptive Surface Fitting Convolution (ASFConv). The key idea is to utilize a set of kernel points distributed on the tangent plane and project them back to point cloud surface. After adapting to the distribution of input points, ASFConv kernel can better capture local neighborhood geometry and benefit the feature extraction. In the experiments, we evaluate our network on two public datasets: ModelNet40 and ShapeNet for classification and segmentation. The experimental results show that our method obtain competitive performances compared to the state-of-the-art. © 2020 IEEE.

关键词： Convolution

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An advanced shape context descriptor based on multi-scale spaces 5

An advanced shape context descriptor based on multi-scale sp...

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5th International Conference on Intelligent Control and Information Processing, ICICIP 2014

作者： Wen-Fei, Wang Gong-Jian, Wen Feng, Gao College of Electronic Science and Technology National University of Defense Technology Changsha Hunan410073 China Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Changsha Hunan410073 China

ISBN: (纸本)9781479936489

Traditional approaches of shape context (SC) descriptor are invariant to object contour noise and shape local slightly deformation, meanwhile, the neighbor radius parameter of shape context model need to be effectively selected without enough apriority knowledge. Additionally, object recognition performance and computational efficiency should be improved in a step future. Therefore, this paper provides an advanced shape context descriptor based on multi-scale spaces. Beyond this method, the shape context descriptor is only extracted from robust contour curvature extremal value point, which is effective to bate the influence of contour noise and local slightly deformation, the neighbor radius parameter is also automatically selected. Comparing with traditional shape matching algorithms, the robustness and the efficiency of this paper approach is tested to be improved distinctly, and the recognition performance is more reliable at the same time. © 2014 IEEE.

关键词： Computational efficiency

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The Video SAR Simulation Algorithm based on the DEM and SBR

The Video SAR Simulation Algorithm based on the DEM and SBR

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IET International Radar Conference 2023, IRC 2023

作者： Zhang, Yongqiang Song, Zhiyong Lei, Gang Xi'an Electronic Engineering Research Institute Xi'an China National Key Laboratory of Science and Technology on Automatic Target Recognition College of Electronic Science National University of Defense Technology Changsha China

ISBN: (纸本)9781839539954

This paper presents a rapid video synthetic aperture radar (SAR) simulation algorithm aimed at addressing the challenge of acquiring video SAR images. The proposed method independently simulates both targets and ground clutter. Background clutter is generated using a digital elevation map (DEM), while target echoes are simulated using Shooting and Bouncing rays (SBR). By employing ray tracing techniques, occlusion areas for both background clutter and targets are computed, thereby ensuring that the simulation results are adaptable to various algorithm development requirements. Ultimately, through a series of fast algorithms and GPU acceleration, significant reduction in simulation time is achieved, enabling rapid simulation of multiple video SAR images. This research provides abundant data sources for the study of video SAR detection, tracking, and recognition algorithms, offering robust support for practical applications. © The Institution of Engineering & technology 2023.

关键词： Clutter (information theory)

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Multiple frames based infrared small target detection method using CNN 4

Multiple frames based infrared small target detection method...

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4th International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2021

作者： Du, Jinming Li, Dongyang Deng, Yingjie Zhang, Luping Lu, Huanzhang Hu, Moufa Shen, Xinglin Liu, Zhuoqun Ji, Xuhuan National Key Laboratory of Science and Technology on Automatic Target Recognition College of Electronic Science and Technology National University of Defense Technology China College of Military Basic Education National University of Defense Technology Changsha China

ISBN: (纸本)9781450385053

In the field of infrared, the detection of dim small target has been a challenging topic. Especially in the complex background, a large number of false alarms might appear when using traditional detection methods, this cannot meet the dual requirements of high detection rate and low false alarm rate. Therefore, in this paper, a CNN based target detection method is proposed for infrared sequence. For an image to be detected, firstly, aiming at the noise and interference which are spatially non-stationary but temporally stable, the method of registration and frame difference is used to suppress the background and extract the target, and the motion information between nearby frames is obtained;next, the extracted information is fed into the light CNN network for the extraction and fusion of different level features;finally, based on the fusion features the small target is detected. The proposed multiple frames based method relies on neural network to extract the motion information in infrared sequence to enhance the distinguishability of the target features under complex condition. Compared with the traditional methods, experimental results show that the proposed CNN method can achieve higher detection rate and lower false alarm rate, especially for the detection of dim small targets in complex background, the advantage is more obvious. © 2021 ACM.

关键词： Errors

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A Robust sub-integer range alignment algorithm against MTRC for ISAR 61471373imaging

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Progress In Electromagnetics Research B 2017年第1期77卷 50904-50904页

作者： Hu, Pengjiang Xu, Shiyou Chen, Zengping The Science and Technology on Automatic Target Recognition Laboratory National University of Defense Technology China

Range alignment plays an important role in the inverse synthetic aperture radar (ISAR) imaging. The performance of the traditional range alignment algorithms decreases when the migration through resolution cells (MTRC) is much severe. In this paper, a measure of MTRC is defined, and the effect of MTRC on range alignment is analyzed. Taking MTRC into account, a robust sub-integer range alignment algorithm is proposed. Firstly, each range profile is interpolated to remove the precision limitation of integer range resolution cell. Subsequently, the matrix formed by all the range profiles is partitioned into several matrix blocks on the slow-time domain. For each matrix block, the range profiles are aligned by minimizing the entropy of the average range profile (ARP). Finally, the matrix blocks are coarsely aligned using the maximum correlation method, followed by a fine alignment based on the minimization of the ISAR image entropy. The effectiveness of the proposed algorithm is validated by simulations and real-world data. Results demonstrate that the proposed method is robust against MTRC and can reduce the alignment error. The resultant ISAR image is much better focused.

关键词： Alignment

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Parallel Two-Stage Multiple Hypotheses Testing Model of Track-Before-Detect 5

Parallel Two-Stage Multiple Hypotheses Testing Model of Trac...

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5th International Conference on Pattern recognition and Artificial Intelligence, PRAI 2022

作者： Du, Jinming Lu, Huanzhang Zhang, Luping Hu, Moufa Shen, Xinglin Li, Dongyang National University of Defense Technology National Key Laboratory of Science and Technology on Automatic Target Recognition Changsha China National University of Defense Technology College of Military Basic Education Changsha China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

Multiple hypotheses testing (MHT)-based Track-before-Detect (TBD) method is a widely known algorithm to detect infrared dim small target. However, the hypotheses space in this kind of method is so big that the computation cost and storage requirement inevitably increase, resulting hardly realized in hardware. To solve the problem, a parallel two stage multiple hypotheses testing model is proposed. First, a parallel MHT model is designed to save the possible trajectories. By using the proposed MHT, all trajectories can be parallelly processed and the trajectories search process is simplified, greatly reducing computation cost. Secondly, for each pixel on the current image, the trajectories search range is divided into 4 different areas, and two-stage MHT is performed in each area to obtain the accumulation energy. By using two-stage strategy, the trajectories number is reduced exponentially, greatly saving storage space. Simulations experimental results show the superiority of the proposed parallel two stage MHT in terms of detection probability and computational complexity under low signal-to-noise ratio (SNR) condition. It can detect the infrared point target when SNR is 1.5 with a detection probability of 90.6% and a false alarm rate of 0.1%. © 2022 IEEE.

关键词： Trajectories

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Few-shot unsupervised domain adaptation for SAR target recognition based on input alignment and class confusion loss 2

Few-shot unsupervised domain adaptation for SAR target recog...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Shu, Wenyu Jiang, Chunyun Zhan, Ronghui Guo, Yue Chen, Shiqi National University of Defense Technology National Key Laboratory of Automatic Target Recognition College of Electronic Science and Technology Changsha China National University of Defense Technology College of Information and Communication Wuhan China

ISBN: (纸本)9798331515669

With the development of simulation technologies and deep learning, utilizing simulated images to train Synthetic Aperture Radar (SAR) target recognition model has become a research hotspot. However, SAR target recognition is hindered by the domain gap between simulated and real images, particularly under few-shot settings which mean only a limited number of unlabeled target domain samples are available for training. Regarding this issue, this paper proposed a new few-shot Unsupervised Domain Adaptation (UDA) method based on input alignment and class confusion loss (named IACC). Our method consists of two modules. The input alignment module (IAM) utilizes a special image-to-image translation model which can realize realistic image translation in few-shot settings, while the Class Confusion Module (CCM) employs a novel loss function to align the feature space and enhance classifier performance. Experimental results on the SAMPLE dataset demonstrate the superior performance of our method in both UDA and few-shot UDA scenarios compared with other existing methods. IACC offers a promising solution for SAR target recognition in challenging domain adaptation scenarios with limited data. © 2024 IEEE.

关键词： Radar simulators

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