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Efficient and Lightweight Target Recognition for High Resolution Spaceborne SAR Images

Efficient and Lightweight Target Recognition for High Resolu...

引用

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

作者： Yu Pan Linbo Tang Donglin Jing Wei Tang Shichao Zhou Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

Fast and reliable target recognition of the synthetic aperture radar (SAR) images has been widely used in the fields of the marine monitoring, military reconnaissance and strike all over the world. However, due to the difficulty of the intra-class difference and inter-class similarity of the multiclass targets in the high resolution SAR images, the existing methods are difficult to recognize the targets accurately when facing the spaceborne platforms with the high resource constraints. Therefore, in order to solve the above problems, we propose a novel recognition method based on the convolutional neural network (CNN). Firstly, we propose a lightweight CNN framework which regards densely connected convolutional network (DenseNet) as the baseline. Secondly, we advocate a strong discriminative loss function which efficiently improves the recognition accuracy of the targets in the spaceborne SAR images. Experiments are conducted on the TerraSAR dataset and MSTAR dataset to evaluate the proposed method. The results show that our method performs better than the baseline on the both benchmark datasets.

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Hyperspectral inversion for soil moisture and temperature based on Gaussian process regression

Hyperspectral inversion for soil moisture and temperature ba...

引用

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

作者： Zhen Li Chenwei Deng Baojun Zhao Yibing Tian Yun Huang Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

The soil moisture and temperature significantly influence the natural environment. Hyperspectral remote sensing can serve as a pivotal technique to monitor soil surface. However, modeling soil parameters encounter the following problems: hyperspectral data is high-dimensional and non-linear, and hyperspectral datasets are of limited size. In this paper, we derive a framework for inversion of soil moisture and temperature. First, wavelet transform is adopted that is able to extract the main structure of spectrum curve and reduce the dimensionality of the hyperspectral data. Then, Gaussian process regression (GPR), which is suitable for small sample data, is applied to predict the soil moisture and temperature. The experimental results show that our model outperforms other methods in estimating soil character.

关键词： soil character hyperspectral inversion wavelet transform Gaussian process regression

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Moving target detection based on Circular Video SAR

Moving target detection based on Circular Video SAR

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

作者： Hongshuo Wang Baojun Zhao Xingsha Yang Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

Video Synthetic Aperture Radar (Video-SAR) achieves radar video observation by continuously monitoring a certain area to form continuous image data of the target, and it plays an important role in civil and military fields. The SAR image of the moving target is defocused and deviates from its true position, but in its real position, a shadow area similar to the target size is generated. In this paper, the shadow area is used to detect the moving target. Firstly, the image sequence is de-noised by wavelet analysis. Then the maximum threshold segmentation of Tsallis gray entropy is performed. The SIFT and FAST algorithms are used to detect and match the corners of each frame. At the same time, the RANSAC algorithm is used to optimize the pairing results to complete the image registration. Finally, the moving target is extracted by the three-frame difference method, and the result is marked to the corresponding position of the original image. Based on the Video SAR segment published by Sandia Labs in the United States, the detection of the largest moving vehicle in the video is realized, and the effectiveness and anti-interference of the proposed algorithm is verified.

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A Novel Method for Abrupt Motion Change Radar Target Detection Based on Generalized Radon-Fourier Transform

A Novel Method for Abrupt Motion Change Radar Target Detecti...

引用

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

作者： Siyuan Liu Zegang Ding Xu Zhou Pengjie You Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

Long-time integration is an effective method to improve the signal-to-noise ratio (SNR) and detection performance of highly maneuvering targets. The existing methods for long-time integration mainly focus on detecting targets with high-order motion under fixed model, losing sight of the possibility that targets may have an abrupt motion change at an unknown instant. Therefore, this paper proposes a novel long-time integration method for highly maneuvering target with abrupt change model (ACM) based on generalized Radon-Fourier transform (GRFT), which could coherently integrate the target echoes before and after ACM and obtain the optimal coherent integration gain. The simulation and experiment results show that the proposed method can detect the ACM target under the condition of extremely low SNR and improve the detection performance compared with the fixed model GRFT.

关键词： abrupt motion change target detection highly maneuvering target detection long-time coherent integration generalized Radon-Fourier transform (GRFT)

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FPGA-based accelerator for convolution operations

FPGA-based accelerator for convolution operations

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

作者： Yunfei Cao Xin Wei Tingting Qiao He Chen Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology 5 South Zhongguancun Street Haidian District Beijing China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

Convolutional neural networks have been widely used in many deep learning applications. Convolutional neural networks have a large number of convolution operations, which poses a huge challenge to real-time performance. In recent years, FPGA implementations of convolutional accelerators have received much attention due to their high performance and energy efficiency. In this paper, we implement an accelerator for convolution operations through the systolic array architecture on Xilinx ZedBoard device. The experimental results show that ours designed accelerators achieving performance density of up to 0.032 Gop/s/DSP.

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A SAR target recognition method with frequency and spatial domain enhancement

A SAR target recognition method with frequency and spatial d...

引用

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

作者： Zhiru Wang Liang Chen Baogui Qi Guanqun Wang Hao Shi Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

Synthetic aperture radar (SAR) image target recognition is a hot issue in remote sensing image application. High accuracy SAR target recognition is truly important in both military and civilian fields. Recently, convolutional neural networks (CNNs) have played an important role in the field of SAR image target recognition, however, most of the existing networks incurs some additional problems such as ignoring the influence of speckle noise on the target recognition process, so the recognition accuracy is low. To cope with these problems, this paper proposes a novel SAR image target recognition method based on CNN with frequency and spatial domain enhancement. First, the image is transformed by Gabor feature descriptor in different frequency directions to generate a plurality of feature maps. Then, the image is spatially enhanced using a Laplace transform. The feature maps which obtained by the frequency and spatial domain enhancement together with the original image are used as the network input. Finally, the improved deep residual network (ResNet) is used to complete the target recognition task. Experimental results demonstrate that the proposed method achieves a state-of-the-art accuracy on the MSTAR dataset.

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Binary Separable Convolutional: An Efficient Fast Image Classification Method

Binary Separable Convolutional: An Efficient Fast Image Clas...

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

作者： Donglin Jing Linbo Tang Yu Pan Wei Tang Shichao Zhou Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

The training and running of neural network require large computational space and memory space, which makes it difficult to deploy on a resource-constrained embedded sys-tems. To address this limitation, we introduce a two-stage pipeline: Depth separable, local binary. Our method is divided into two steps. Firstly, a deep separable convolution is applied to each input channel. Then, point-by-point convolution is applied to the feature map obtained by the filter. In the second step, we use local binarization method to initialize the filter corresponding to the input channel into sparse binary. In network training, the sparse binary filter remains fixed and only needs to train convolution of 1×1 size. Our experimental results show that, on the basis of approximate accuracy with the original network, we have reduced the number of convolution parameters by 9x to 10x, and reduced the training time and testing time to by 2x. Our compression method helps to deploy complex neural networks on resource-constrained embedded platform.

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FPGA-based reconfigurable matrix inversion implementation for inverse filtering of multichannel SAR imaging

FPGA-based reconfigurable matrix inversion implementation fo...

引用

IET International Radar Conference 2018, IRC 2018

作者： Li, HuiXing Feng, YangKai Hu, ShanQing Li, BingYi Xie, YiZhuang Wu, MengChao 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 Shanghai Institute of Satellite Engineering Shanghai China

In multi-channel synthetic aperture radar (SAR), the azimuth non-uniform sampling tends to result in a large number of virtual point targets, which are not expected. Inverse filter algorithm provides a new idea for solving this problem. This way can be abstracted as a matrix inversion in essence, which becomes the key factor that affects the real-time and accuracy of multi-channel pre-processing. This study presents the implementation of matrix inversion method on field programmable gate array (FPGA), based on lower and upper triangular matrix (LU) decomposition algorithm. In this process, the efficient parallelism of FPGA and the rich floating-point intellectual property (IP) cores are fully utilised to speed up the process of inverting the matrix with a data type of 32-bit single-precision floating-point. In this design, the parallelism of the algorithm was fully considered and a hierarchical iterative processing strategy was adopted to realise the reconfigurable storage and computing unit both. At the same time, in order to achieve the balance of resources and efficiency, a reusable structure was proposed also, using the pipeline technology and appropriate data scheduling. Finally, Modelsim platform is used to observe the simulation results, and the performance can be detected combined with MATLAB platform. At last, the computational accuracy is up to 10−7, and the speedup ratio can reach about 1.461 × 103 © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： MATLAB

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Ground Target Automatic Recognition and Tracking in Complex Battlefield Environment

Ground Target Automatic Recognition and Tracking in Complex ...

引用

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

作者： Chen Wu Linbo Tang Chenhui Duan Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

With the developments of science and the changes of modern warfare modes, the precise guidance and attack towards hostile targets in complex battlefield environments has extremely important military needs and research significance. However, the existing guidance mode is often constrained by factors such as battlefield background interference, target appearance variation and limited data links, which in turn affects tracking and strike performance. To this end, we present an integrated recognition and tracking scheme for the ground targets in complex battlefield environment. Specifically, with the missile-target distance information, we design an adaptive scale estimation scheme to tackle the aspect ratio variation during tracking. Furthermore, an efficient tracking monitoring index is advocated to judge the tracking condition as well as forecast the potential tracking failure. Once tracking failure is determined, a deep-learning based detection module is activated to re-detect the target as well as correct the tracking drift. In the strike stage of the mission, the key region of the hostile target would be identified and locked by the missile in order to maximize the attack effect. The proposed recognition and tracking scheme are tested upon extensive actual videos and achieved a tracking accuracy over 85%, which demonstrates its effectiveness as well as provides a novel solution for accurate and robust attack on enemy targets in a complex battlefield environment.

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Weighted Anomaly Detection Algorithm Based on Local Density

Weighted Anomaly Detection Algorithm Based on Local Density

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

作者： Yibing Tian Linbo Tang Zhen Li Yun Huang Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

Anomaly detection for hyperspectral imagery (HSI) has drawn a lot of attention lately. In the past decades, several methods for detecting anomalies in HSI have been developed. The Reed-Xiaoli detector (RXD) is one of the most widely used methods for anomaly detection. However, RXD assumes that background follow the Gaussian distribution, not always conformed to the real case. In order to solve this problem, we improve the RX anomaly detection algorithm by weighting changes of background. First, the concentric double-window model is utilized to obtain the local densities of each background pixels. Then, the local densities are applied to weight background mean and covariance matrix to reduce the influence of abnormal pixels in the background. The experimental results show that our method outperforms other approaches in anomaly detection.

关键词： hyperspectral imagery anomaly detection local RXD weighted covariance matrix anomaly detection hyperspectral imagery AKR1B10 gene variance covariance matrix Gaussian distribution

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