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arXiv 2018年

作者： Fu, Kun Guo, Zhi Yan, Menglong Sun, Xian Sun, Hao Yang, Xue Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

—Ship detection is of great importance and full of challenges in the field of remote sensing. The complexity of application scenarios, the redundancy of detection region, and the difficulty of dense ship detection are all the main obstacles that limit the successful operation of traditional methods in ship detection. In this paper, we propose a brand new detection model based on multiscale rotational region convolutional neural network to solve the problems above. This model is mainly consist of five consecutive parts: Dense Feature Pyramid Network (DFPN), adaptive region of interest (ROI) Align, rotational bounding box regression, prow direction prediction and rotational nonmaximum suppression (R-NMS). First of all, the low-level location information and high-level semantic information are fully utilized through multiscale feature networks. Then, we design adaptive ROI Align to obtain high quality proposals which remain complete spatial and semantic information. Unlike most previous approaches, the prediction obtained by our method is the minimum bounding rectangle of the object with less redundant regions. Therefore, rotational region detection framework is more suitable to detect the dense object than traditional detection model. Additionally, we can find the berthing and sailing direction of ship through prediction. A detailed evaluation based on SRSS and DOTA dataset for rotation detection shows that our detection method has a competitive performance. Copyright © 2018, The Authors. All rights reserved.

关键词： Remote sensing

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Electromagnetic Simulation Aided SAR Target Classification Via Deep Domain Adaptation

Electromagnetic Simulation Aided SAR Target Classification V...

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IEEE International Conference on Radar

作者： Xiaoling Lv Xiaolan Qiu Wenming Yu Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Suzhou Aerospace Information Research Institute Suzhou China State key Laboratory of Millimeter Waves Southeast University Nanjing China

ISBN: (纸本)9781665468893

Convolutional neural networks (CNNs) have made tremendous success in optical images classification recently. However, in synthetic aperture radar (SAR) target classification, it is difficult to annotate a large amount of real SAR images to train CNNs. Sufficient annotated images can be easily obtained through simulation, but the disparity between the simulated images and the real images makes them difficult to directly apply to the real images classification. In this paper, we propose a model that integrates multi-kernel maximum mean discrepancy (MK-MMD) and domain-adversarial training to alleviate this problem. Simulated SAR images with annotation and unlabeled real SAR images are used to train our model. First, we use domain-adversarial training to prompt the model to extract domain-invariant features. Then, the MK-MMD between the hidden representations of simulated images and real images is reduced to narrow domain discrepancy. Experimental results on the real SAR dataset demonstrate that our method effectively solves the domain shift problem and improves the classification accuracy.

关键词： Training Adaptation models Optical fiber networks Feature extraction Optical imaging Radar polarimetry Data models

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Miniaturization Design of TEM Horn Antenna for Ground-Penetrating Radar

Miniaturization Design of TEM Horn Antenna for Ground-Penetr...

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International Conference on Microwave and Millimeter Wave technology Proceedings

作者： Zhi-Hao Li Jing Ma Jin Wu Jia-Jia Huang Yong-Han Lu Lin Peng Key Laboratory of Cognitive Radio and Information Processing (Ministry of Education) Guilin University of Electronic Technology Guilin China Education Department of Guangxi Zhuang Autonomous Region Key Laboratory of Microwave and Optical Wave Application Technology (Guilin University of Electronic Technology) Guilin China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing China

This paper presents a miniaturized TEM horn antenna for ground penetrating radar (GPR). First, an exponential gradient horn antenna is designed with a low cut-off frequency of 0.45 GHz. Then, the low cut-off frequency is decreased to 0.19 GHz through curve structural loading and lens loading without increasing the size of the horn. The antenna size is 378.49 mm × 359.07 mm × 386.06 mm, corresponding to 0.226 λ 1 × 0.238 λ 1 × 0.243 λ 1 ( λ 1 is the wavelength corresponding to the lowest frequency of the antenna). The results show that the working bandwidth of the antenna is 0.19-3GHz, and it has a good radiation pattern in the band, with a peak gain of 12.4dBi, which would be a good candidate for GPR.

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Wider channel attention network for remote sensing image super-resolution

arXiv

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arXiv 2018年

作者： Gu, Jun Xu, Guangluan Zhang, Yue Sun, Xian Wen, Ran Wang, Lei Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

Recently, deep convolutional neural networks (CNNs) have obtained promising results in image processing tasks including super-resolution (SR). However, most CNN-based SR methods treat low-resolution (LR) inputs and features equally across channels, rarely notice the loss of information flow caused by the activation function and fail to leverage the representation ability of CNNs. In this letter, we propose a novel single-image super-resolution (SISR) algorithm named Wider Channel Attention Network (WCAN) for remote sensing images. Firstly, the channel attention mechanism is used to adaptively recalibrate the importance of each channel at the middle of the wider attention block (WAB). Secondly, we propose the Local Memory Connection (LMC) to enhance the information flow. Finally, the features within each WAB are fused to take advantage of the network’s representation capability and further improve information and gradient flow. Analytic experiments on a public remote sensing data set (UC Merced) show that our WCAN achieves better accuracy and visual improvements against most state-of-the-art methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Image fusion

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Design of UHF Low-profile Monopole Antenna for Ground Penetrating Radar

Design of UHF Low-profile Monopole Antenna for Ground Penetr...

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International Conference on Microwave and Millimeter Wave technology Proceedings

作者： Jia-Jia Huang Jing Ma Zhi-Hao Li Yong-Han Lu Li-Wei Guo Lin Peng Key Laboratory of Cognitive Radio and Information Processing (Ministry of Education) Guilin University of Electronic Technology Guangxi China Education Department of Guangxi Zhuang Autonomous Region Key Laboratory of Microwave and Optical Wave Application Technology Guilin University of Electronic Technology Guangxi China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing China

A low-profile and ultra-wideband unidirectional monopole antenna with absorbing material loading is proposed for ground-penetrating radar (GPR) applications in the UHF band. The overall dimensions of the antenna is 0.472λ 0 × 0.152λ 0 ×0.006λ 0 , where λ 0 is the free-space wavelength at the lowest frequency of 0.38GHz. The monopole antenna with low profile operates in the 0.38GHz-3GHz band with standing-wave ratio<2, a realized gain between -8.13 and 0.04 dBi. Good unidirectional radiation patterns were obtained in the whole band. The composed array antenna can be applied to the transmit signal and receive signal modules of the system.

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ATASI-Net: An Efficient Sparse Reconstruction Network for Tomographic SAR Imaging with Adaptive Threshold

arXiv

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arXiv 2022年

作者： Wang, Muhan Zhang, Zhe Qiu, Xiaolan Gao, Silin Wang, Yue Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology China Suzhou Aerospace Information Research Institute Jiangsu Suzhou China Aerospace Information Research Institute Chinese Academy of Sciences China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China The Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Electrical and Computer Engineering Department George Mason University FairfaxVA United States

Tomographic SAR technique has attracted remarkable interest for its ability of three-dimensional resolving along the elevation direction via a stack of SAR images collected from different cross-track angles. The emerged compressed sensing (CS)-based algorithms have been introduced into TomoSAR considering its super-resolution ability with limited samples. However, the conventional CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity, and complex parameter fine-tuning. Aiming at efficient TomoSAR imaging, this paper proposes a novel efficient sparse unfolding network based on the analytic learned iterative shrinkage thresholding algorithm (ALISTA) architecture with adaptive threshold, named Adaptive Threshold ALISTA-based Sparse Imaging Network (ATASI-Net). The weight matrix in each layer of ATASI-Net is pre-computed as the solution of an offline optimization problem, leaving only two scalar parameters to be learned from data, which significantly simplifies the training stage. In addition, adaptive threshold is introduced for each azimuth-range pixel, enabling the threshold shrinkage to be not only layer-varied but also element-wise. Moreover, the final learned thresholds can be visualized and combined with the SAR image semantics for mutual feedback. Finally, extensive experiments on simulated and real data are carried out to demonstrate the effectiveness and efficiency of the proposed method. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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A W-band All-Dielectric Lens Antenna with Flat-Top Radiation Pattern

A W-band All-Dielectric Lens Antenna with Flat-Top Radiation...

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International Conference on Microwave and Millimeter Wave technology Proceedings

作者： Yong-Han Lu Jing Ma Zhi-Hao Li Jia-Jia Huang Kun-Shan Mo Lin Peng Key Laboratory of Cognitive Radio and Information Processing (Ministry of Education) Guilin University of Electronic Technology Guilin China Education Department of Guangxi Zhuang Autonomous Region Key Laboratory of Microwave and Optical Wave Application Technology (Guilin University of Electronic Technology) Guilin China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing China

This paper proposes a W-band all-dielectric lens antenna with the characteristic flat-top beam. The lens loaded on the horn is composed of 10 concentric elliptical rings and a tapered matching layer. Its geometries are optimized by the non-dominated sorting genetic algorithm II (NSGA-II). The lens antenna designed at 92-97 GHz achieves a beamwidth of 40° with ripple level less than 1 dB in E-plane and H-plane. In addition, the proposed antenna can be prototyped easily using 3D-printing technology with low cost.

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Real-time correlation tracking via joint model compression and transfer

arXiv

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arXiv 2019年

作者： Wang, Ning Zhou, Wengang Song, Yibing Ma, Chao Li, Houqiang CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei China Tencent AI Lab Shenzhen China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

Correlation filters (CF) have received considerable attention in visual tracking because of their computational efficiency. Leveraging deep features via off-the-shelf CNN models (e.g., VGG), CF trackers achieve state-of-the-art performance while consuming a large number of computing resources. This limits deep CF trackers to be deployed to many mobile platforms on which only a single-core CPU is available. In this paper, we propose to jointly compress and transfer off-the-shelf CNN models within a knowledge distillation framework. We formulate a CNN model pretrained from the image classification task as a teacher network, and distill this teacher network into a lightweight student network as the feature extractor to speed up CF trackers. In the distillation process, we propose a fidelity loss to enable the student network to maintain the representation capability of the teacher network. Meanwhile, we design a tracking loss to adapt the objective of the student network from object recognition to visual tracking. The distillation process is performed offline on multiple layers and adaptively updates the student network using a background-aware online learning scheme. Extensive experiments on five challenging datasets demonstrate that the lightweight student network accelerates the speed of state-of-the-art deep CF trackers to real-time on a single-core CPU while maintaining almost the same tracking accuracy. Copyright © 2019, The Authors. All rights reserved.

关键词： Object recognition

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Influencing Factors of Housing Sales Vacancy in the Guangzhou Metropolitan Area,China

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Journal of Architectural Research and Development 2021年第6期5卷 49-54页

作者： Xiaoli Yue Yang Wang Hong’ou Zhang Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and ApplicationGuangzhou Institute of GeographyGuangdong Academy of SciencesGuangzhou 510070Guangdong ProvinceChina School of Architecture and Urban Planning Guangdong University of TechnologyGuangzhou 510090Guangdong ProvinceChina Faculty of Geography Yunnan Normal UniversityKunming 650500Yunnan ProvinceChina

In this paper,nine indicators selected from three perspectives(convenience,environmental and location characteristics)and three regression models(OLS,SLM and SEM)are used to explore the influencing factors of housing sales vacancy in the Guangzhou Metropolitan Area,*** results show that subway accessibility,peripheral aversion municipal facilities and distance from the CBD(Central Business District)are consistent with theoretical *** accessibility is negatively correlated with the housing sales vacancy rates,while peripheral aversion municipal facilities and distance from the CBD are positively correlated with housing vacancy rates.

关键词： Housing vacancy Regression models Sales vacancy Influencing factors Guangzhou Metropolitan Area

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A Pylon Detection Method Based on Faster R-CNN in High-Resolution SAR Images

A Pylon Detection Method Based on Faster R-CNN in High-Resol...

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Asian and Pacific Conference on Synthetic Aperture Radar (APSAR)

作者： Lv Xiaoling Jiankun Chen Xiaolan Qiu Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Aerospace Information Research Institute University of Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728173344

SyntheticAperture Radar (SAR) is an active microwave imaging system, which can provide all-time and allweather imaging with a wide observation range. In sub-meter high-resolution SAR images, man-made metallic targets, such as pylons, can be easily detected and their details can be obtained. Therefore, pylon detection using high-resolution SAR images has its unique advantages. To solve the problem that traditional pylon detection methods in SAR images require the manual design of feature extractors and perform poorly in terms of realtime requirements, an effective and fast framework for pylon detection in SAR images is proposed in this paper. It combines Faster R-CNN and sliding window algorithm to detect pylons in large-scene SAR images. Besides, it effectively improves detection accuracy by using data augmentation. The proposed method can detect up to 97.6% on the test set and 80% in largescene GF-3 SAR images. The average time cost of detection is 0. 06s for a slice image and 90s for a large-scale SAR image. Experiments demonstrate good performance in detection rate and instantaneity.

关键词： Costs Poles and towers Manuals Radar imaging Feature extraction Microwave theory and techniques Radar polarimetry

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