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检索条件"机构=Beijing Key Laboratory of Embedded Real-Time Information Processing Technology"

共 577 条记录，以下是31-40 订阅

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Binary Separable Convolutional: An Efficient Fast Image Classification Method

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

引用

2019 IEEE International Conference on Signal, information and Data processing, ICSIDP 2019

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

ISBN: (纸本)9781728123455

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 11 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. © 2019 IEEE.

关键词： Image classification

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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

2019 IEEE International Conference on Signal, information and Data processing, ICSIDP 2019

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

ISBN: (纸本)9781728123455

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. © 2019 IEEE.

关键词： Soil temperature

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Accurate non-contact vibration amplitude retrieval using least squares estimation

Accurate non-contact vibration amplitude retrieval using lea...

引用

IET International Radar Conference 2015

作者： Hu, C. Li, A. Wang, R. Zeng, T. Liu, W. Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9781785610387

An accurate non-contact amplitude retrieval of the periodic movement is proposed in this paper. Firstly, the backscattered signal of a vibrational target can be expanded as a series of harmonics whose amplitudes are determined by the vibration amplitude. Then, based on the least squares estimation, all the available harmonics are utilized to suppress the influence of the signal noise on the vibration measurement, so as to improve the accuracy of vibration amplitude retrieval. Finally, the Monte Carlo simulation is performed to validate the effectiveness in the case of low signal-to-noise ratio (-9dB). Accuracy of the new method in different cases of signal-to-noise ratio is also obtained. In addition, the real experimental data acquired by a 100GHz millimeter radar is used to test the performance of the proposed method, associated with a calibrator of vibration. In the experiment, a calibrator of vibration is 30m away from the radar. The results show the proposed method has better accuracy than the ones in the case of only one harmonic pair is used.

关键词： Signal to noise ratio

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Moving target detection based on Circular Video SAR

Moving target detection based on Circular Video SAR

引用

2019 IEEE International Conference on Signal, information and Data processing, ICSIDP 2019

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

ISBN: (纸本)9781728123455

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. © 2019 IEEE.

关键词： Synthetic aperture radar

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A novel subsidence monitoring approach based on multi-GNSS illuminator bistatic interferometry

A novel subsidence monitoring approach based on multi-GNSS i...

引用

IET International Radar Conference 2015

作者： Zeng, T. Tian, W.M. Zhang, T. Hu, C. Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9781785610387

This paper proposes a novel subsidence monitoring method. The method utilizes multi Global Navigation Satellite System (GNSS) as illuminators, a receiver employed in reference station and a transponder set in the region of interest as a measurement station to construct the real time subsidence monitoring system. Exploiting the commonness of the errors in both the reference station and the measurement one, extending the principle of traditional differential GNSS (DGNSS) to a radar-transponder counterpart, a low cost and high accuracy system with 3 dimensional deformations estimation ability can be realized. Simulation is conducted to analyze the performance and validate the proposed method. The result shows that the proposed method is able to achieve sub-centimeter accuracy in short coherence time, promising its potential application in some security fields such as the high-speed railway roadbed subsidence monitoring.

关键词： Deformation

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A Fast MART Method with Voxel Filtering for GEO SAR-Based Computerized Ionospheric Tomography 2

A Fast MART Method with Voxel Filtering for GEO SAR-Based Co...

引用

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

作者： Sui, Yi Dong, Xichao Chen, Zhiyang Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China

ISBN: (纸本)9798331515669

Ionospheric monitoring is crucial for space environment assessment and the success of space mission. Traditional computerized ionospheric tomography (CIT) based on Global Navigation Satellite System (GNSS) are constrained ground receivers, while CIT based on geosynchronous synthetic aperture radar (GEO SAR) offers an ionospheric tomography solution that does not rely on ground receivers. This approach leverages the abundant Permanent Scatterer (PS) points in the SAR image for electron density reconstruction, providing strong adaptability to various environments. However, due to the large number of PS points in the large coverage area of GEO SAR and the abundance of total electron density (TEC) data, the conventional multiplicative algebraic reconstruction technique (MART) is inefficient in processing iteration. To this end, this paper proposes a fast MART method based on Voxel Filtering, and verifies the effectiveness of the proposed method through a tomographic experiment. The results show that the proposed method ensures reliable tomographic results, effectively improves the iteration speed, and significantly improves the computational efficiency, which provides strong support for GEO SAR-based CIT with huge TEC data. © 2024 IEEE.

关键词： Ionospheric measurement

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Ground-based SAR imaging algorithm based on keystone formatting

Ground-based SAR imaging algorithm based on keystone formatt...

引用

IET International Radar Conference 2015

作者： Mao, Cong He, Qian Tian, Weiming Hu, Cheng Zeng, Tao Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9781849199940

In this paper, a novel imaging algorithm tailored for GB-SAR data is proposed. The characteristics of GB-SAR data are firstly analyzed, and then a signal model (after range compression) is analytically built up. According to this model, our imaging algorithm, based on keystone formatting and range-Doppler domain subblock division, is then put forward, its azimuth focusing is essentially achieved through a space-variant dechirp operation. This algorithm can be widely applied to near-field and far-field as well as wide view angle GB-SAR data sets. Moreover, the computational cost of this algorithm is quite low. The image is formatted in a polar-like coordinate which is well fit for GB-SAR data. The algorithm is extensively validated both with numerical simulations and real GB-SAR data.

关键词： Synthetic aperture radar

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Ship Detection in Synthetic Aperture Radar Imagery Based on Discriminative Dictionary Learning 6

Ship Detection in Synthetic Aperture Radar Imagery Based on ...

引用

6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Wang, Yun Chen, Liang Shi, Hao Zhang, Bocheng Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9781728129129

The ship target detection technology based on SAR image has important significance in military and civil field applications and is one of the research hotspots at this stage. In this paper, the research work on typical problems in SAR image ship target detection is carried out. A ship target detection algorithm based on discriminative dictionary learning is proposed, which mainly includes image denoising, candidate region extraction and candidate region identification. Firstly, an adaptive non-local filtering method is used to denoise the SAR image. Then the gradient feature map reconstruction algorithm is used to extract the candidate regions. Finally, the category constrained discriminative dictionary learning method is used to classify the candidate regions. The algorithm is based on GF-3 and Terra SAR data. The experimental results show that the proposed algorithm has strong robustness and adaptability. © 2019 IEEE.

关键词： Synthetic aperture radar

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Weighted Anomaly Detection Algorithm Based on Local Density

Weighted Anomaly Detection Algorithm Based on Local Density

引用

2019 IEEE International Conference on Signal, information and Data processing, ICSIDP 2019

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

ISBN: (纸本)9781728123455

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. © 2019 IEEE.

关键词： Covariance matrix

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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

2019 IEEE International Conference on Signal, information and Data processing, ICSIDP 2019

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

ISBN: (纸本)9781728123455

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. © 2019 IEEE.

关键词： Synthetic aperture radar

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