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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Ankai Hou Mingcang Zhu Pengshan Li Yong He Xiaobo Zhang Jibao Shi Kai Chen Tao Weng Zezhong Zheng Guoqing Zhou School of Resources and Environment University of Electronic Science and Technology of China Chengdu Sichuan PRC Chengdu Sichuan PRC Chengdu Land Planning and Cadastre Center Chengdu Sichuan PRC Sichuan Research Institute for Eco-system Restoration & Geo-disaster Prevention Chengdu Sichuan PRC Chengdu Institute of Survey & Investigation Chengdu Sichuan PRC Guangxi Key Laboratory for Spatial Information and Geomatics Guilin University of Technology Guilin Guangxi PRC

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

With the rapid development of urbanization, more and more attention has been paid to the structure of urban function zone. Thus, it is of great significance to investigate urban function zone. In this paper, we introduced the deep neural network (DNN) to infer the urban function zone with a supervised classification approach, taking the Shenzhen city in China as a case. First of all, the urban road networks of Shenzhen city were gathered and selected appropriately. Then, the fifth level road networks were utilized to segment the study region. Second, the communication data of different times and points of interest (POI) were collected. Then, the fifteen factors influencing urban function zone were derived. In addition, the urban function zone was divided into five types and the labeled examples with fifteen influencing factors were chosen. Third, the labeled examples were employed to train the DNN with different hidden layers compared with random forest (RF) and support vector machine (SVM). The models were trained with the approach of five-fold cross validation, and the average training accuracy with five times is taken as the accuracy of models. Finally, this paper compared the accuracy. It's been shown in the results that DNN was the optimum model and achieved the highest accuracy. Therefore, our proposed method is an efficient approach to infer the urban function zone.

关键词： Support vector machines Radio frequency Urban areas Social networking (online) Roads Random forests Deep learning

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Ship Detection with Sar Based on Yolo

Ship Detection with Sar Based on Yolo

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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Shaobin Jiang Mingcang Zhu Yong He Zezhong Zheng Fangrong Zhou Guoqing Zhou School of Resources and Environment University of Electronic Science and Technology of China Chengdu Sichuan PRC Chengdu Sichuan PRC Sichuan Research Institute for Eco-system Restoration & Geo-disaster Prevention Chengdu Sichuan PRC Guangxi Key Laboratory for Spatial Information and Geomatics Guilin University of Technology Guilin Guangxi PRC Electric Power Research Institute Yunnan Power Grid Co. Ltd. Kunming Yunnan PRC

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Synthetic aperture radar (SAR) allows all-weather, day and night surveillance. Thus, it is of great significance for the ship detection and recognition. Because of the SAR special imaging mechanism, it is very difficult to extract the ship features with SAR image for the traditional target detection algorithm. In this paper, we proposed a approach which is composed of you only look once (YOLO) algorithm, sliding window detection strategy, and clustering algorithm. Firstly, the SAR images of GaoFen-3 and training dataset are gathered. Secondly, the experiments about the size of ship detection frame is carried out to find the optimum size of the frame for the training model. Thirdly, the ships are detected initially with YOLO v3 and fast region-based convolutional neural network (Fast-RCNN). Finally, the detected ships are clustered adaptively, and the experimental results of YOLO v3 and Fast-RCNN are compared and discussed at length. Our experimental results demonstrated that our method outperformed Fast-RCNN to detect the ships in the surface sea with low-resolution wide -band SAR images. Therefore, our approach is a robust method to detect the ships in the surface sea with SAR images.

关键词： Marine vehicles Synthetic aperture radar Radar polarimetry Training Feature extraction Object detection Deep learning

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Tracking Performance of Networked systems with Limited Bandwidth and Colored Noise

Tracking Performance of Networked Systems with Limited Bandw...

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Chinese Automation Congress (CAC)

作者： Bin Zhang Xiaowei Jiang Li Sun Xianhe Zhang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China School of Statistics and Mathematics Zhongnan University of Economics and Law Wuhan China School of Electrical Engineering and Automation Hubei Normal University Huangshi China

This study investigated the optimal tracking performance (OTP) of multi-input multi-output (MIMO), discrete- time networked control systems (NCSs). The limits of tracking performance (TP) under the influences of bandwidth, encoding- decoding, and additive coloured Gaussian noise (ACGN) are derived using the techniques of coprime decomposition and all-pass decomposition. The results reveal the negative impact of non-minimum phase (NMP) zeros and unstable poles of the plant as well as network communication constraints on the TP of NCSs. Finally, a numerical simulation is discussed and verifies our conclusions.

关键词： Networked control systems Gaussian noise Bandwidth Numerical simulation Downlink Decoding Colored noise

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiaoke Zhu Danyang Li Xiaopan Chen Fumin Qi Fan Zhang Xiao-Yuan Jing School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China Department of System Operation National Supercomuting Center in Shenzhen Shenzhen China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer Science Wuhan University Wuhan China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Facial image based kinship verification aims to decide whether there exists kinship between the given facial images. In practice, the cross-generation differences will cause adverse effects on kinship verification, which limits the performance. Therefore, how to mine the implied similarity from facial images with large cross-generation divergence is an important problem in kinship verification, which has not yet been well studied. In view of this, we propose a Similarity Mining via Implicit matching pattern LEarning (SMILE) approach for kinship verification. Specifically, SMILE mainly consists of two modules, including a Semi-coupled Multi-pattern Similarity Learning (SMSL) module and a Cross-Generation Feature Normalization (CGFN) module. The SMSL module is designed to learn multiple semi-coupled matching patterns for mining the implicit facial similarity information from different perspectives. The CGFN module aims to reduce the divergence between facial images of parent and child. Extensive experiments demonstrate that the proposed approach outperforms the existing state-of-the-art methods.

关键词： Pattern matching Similarity learning

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Quantum machine learning of eco-environmental surfaces

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科学通报（英文版） 2022年第10期67卷 1031-1033页

作者： Tianxiang Yue Yi Liu Zhengping Du John Wilson Dongyi Zhao Yu Wang Na Zhao Wenjiao Shi Zemeng Fan Xiaomin Zhao Qin Zhang Hongsheng Huang Qingyuan Wu Wei Zhou Yimeng Jiao Zhe Xu Saibo Li Yang Yang Bojie Fu State Key Laboratory of Resources and Environment Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China College of Resources and Environment University of Chinese Academy of SciencesBeijing 100190China College of Land Resources and Environment Jiangxi Agricultural UniversityNanchang 330045China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China State Key Laboratory of Resources and Environment Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China College of Resources and Environment University of Chinese Academy of SciencesBeijing 100190China State Key Laboratory of Resources and Environment Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China State Key Laboratory of Resources and Environment Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China College of Resources and Environment University of Chinese Academy of SciencesBeijing 100190China Spatial Sciences Institute University of Southern CaliforniaLos AngelesCA 90089-0374USA Origin Quantum Computing Technology Company Limited Hefei 230093China Yanqi Lake Beijing Institute of Mathematical Sciences and Applications Beijing 101408China College of Land Resources and Environment Jiangxi Agricultural UniversityNanchang 330045China Monitoring Station for Weather and Eco-Environment of WuGong Mountain Pingxiang 337000China School of Geographical Sciences Southwest UniversityChongqing 400715China Research Center for Eco-Environmental Sciences Chinese Academy of SciencesBeijing 100085China Faculty of Geographical Science Beijing Normal UniversityBeijing 100875China

The computer advances of the past century can be traced to the increase in their numbers on chips that has accompanied the miniaturization of ***,computers are nearing the fundamental limits of such miniaturization[1].Many practical problems require huge amounts of computational resources that exceed the capabilities of today's computers.A 54-qubit quantum computer on the other hand can solve in minutes a problem that would take a classical machine 10,000 years[2].

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NTIRE 2020 Challenge on Image and Video Deblurring

arXiv

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arXiv 2020年

作者： Seungjun, Nah Sanghyun, Son Radu, Timofte Kyoung Mu, Lee Tseng, Yu Xu, Yu-Syuan Chiang, Cheng-Ming Tsai, Yi-Min Brehm, Stephan Scherer, Sebastian Xu, Dejia Chu, Yihao Sun, Qingyan Jiang, Jiaqin Duan, Lunhao Yao, Jian Purohit, Kuldeep Suin, Maitreya Rajagopalan, A.N. Ito, Yuichi Hrishikesh, P.S. Puthussery, Densen Akhil, K.A. Jiji, C.V. Kim, Guisik Deepa, P.L. Xiong, Zhiwei Huang, Jie Liu, Dong Kim, Sangmin Nam, Hyungjoon Kim, Jisu Jeong, Jechang Huang, Shihua Fan, Yuchen Yu, Jiahui Yu, Haichao Huang, Thomas S. Zhou, Ya Li, Xin Liu, Sen Chen, Zhibo Dutta, Saikat Das, Sourya Dipta Garg, Shivam Sprague, Daniel Patel, Bhrij Huck, Thomas Department of ECE ASRI SNU Korea Republic of Computer Vision Lab ETH Zurich Switzerland MediaTek Inc University of Augsburg Chair for Multimedia Computing and Computer Vision Lab Germany Peking University China Beijing University of Posts and Telecommunications China Beijing Jiaotong University China Wuhan University China Indian Institute of Technology Madras India Vermilion College of Engineering Trivandrum India CVML Chung-Ang University Korea Republic of APJ Abdul Kalam Technological University India University of Science and Technology of China China Image Communication Signal Processing Laboratory Hanyang University Korea Republic of Southern University of Science and Technology China University of Illinois at Urbana-Champaign United States CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China China IIT Madra Jadavpur University India University of Texas Austin United States Duke University Computer Science Department United States

Motion blur is one of the most common degradation artifacts in dynamic scene photography. This paper reviews the NTIRE 2020 Challenge on Image and Video Deblurring. In this challenge, we present the evaluation results from 3 competition tracks as well as the proposed solutions. Track 1 aims to develop single-image deblurring methods focusing on restoration quality. On Track 2, the image deblurring methods are executed on a mobile platform to find the balance of the running speed and the restoration accuracy. Track 3 targets developing video deblurring methods that exploit the temporal relation between input frames. In each competition, there were 163, 135, and 102 registered participants and in the final testing phase, 9, 4, and 7 teams competed. The winning methods demonstrate the state-of-the-art performance on image and video deblurring tasks. Copyright © 2020, The Authors. All rights reserved.

关键词： Image enhancement

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A fundamental theorem for eco-environmental surface modelling and its applications

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Science China Earth Sciences 2020年第8期63卷 1092-1112页

作者： Tianxiang YUE Na ZHAO Yu LIU Yifu WANG Bin ZHANG Zhengping DU Zemeng FAN Wenjiao SHI Chuanfa CHEN Mingwei ZHAO Dunjiang SONG Shihai WANG Yinjun SONG Changqing YAN Qiquan LI Xiaofang SUN Lili ZHANG Yongzhong TIAN Wei WANG Ying’an WANG Shengnan MA Hongsheng HUANG Yimin LU Qing WANG Chenliang WANG Yuzhu WANG Ming LU Wei ZHOU Yi LIU Xiaozhe YIN Zong WANG Zhengyi BAO Miaomiao ZHAO Yapeng ZHAO Yimeng JIAO Ufra NASEER Bin FAN Saibo LI Yang YANG John PWILSON State Key L aboratory of Resources and Environment Information System Institute of Geographical Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China College of Resources and Environment University of Chinese Academy of SciencesBeijing 100049China College of Land Resources and Environment Jiangxi Agricultural UniversityNanchang 330045China College of Geomatics Shandong University of Science and TechnologyQingdao 266590China College of Forestry Beijing Forestry UniversityBeijing 100083China School of Land and Resources China West Normal UniversityNanchong 637002China College of Geographical Information and Tourism Chuzhou UniversityChuzhou 239000China Institutes of Science and Development Chinese Academy of SciencesBeijing 100190China Department of Information Engineering Shandong University of Science and TechnologyTai'an 271019China College of Resources Sichuan Agricultural UniversityChengdu 611130China College of Geography and Tourism Qufu Normal UniversityRizhao 276828China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100101China School of Geographical Science Southwest UniversityChongqing 400715China National Disaster Reduction Center of China Ministry of Emergency ManagementBeijing 100721China Department for Population Data China Population and Development Research CenterBeijing 100081China Department for Industrial Standard China Standardization AdministrationBeijing 100088China The Academy of Digital China Fuzhou UniversityFuzhou 350002China National Institute of Environmental Health Chinese Center for Disease Control and PreventionBeijing 100021China School of Information Engineering China University of GeoscienceBeijing 100083China Qian Xuesen Laboratory of Space Technology Beijing 100094China College of Architecture and Urban Planning Chongqing Jiaotong UniversityChongqing 400074China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing

We propose a fundamental theorem for eco-environmental surface modelling(FTEEM) in order to apply it into the fields of ecology and environmental science more easily after the fundamental theorem for Earth’s surface system modeling(FTESM). The Beijing-Tianjin-Hebei(BTH) region is taken as a case area to conduct empirical studies of algorithms for spatial upscaling, spatial downscaling, spatial interpolation, data fusion and model-data assimilation, which are based on high accuracy surface modelling(HASM), corresponding with corollaries of FTEEM. The case studies demonstrate how eco-environmental surface modelling is substantially improved when both extrinsic and intrinsic information are used along with an appropriate method of HASM. Compared with classic algorithms, the HASM-based algorithm for spatial upscaling reduced the root-meansquare error of the BTH elevation surface by 9 m. The HASM-based algorithm for spatial downscaling reduced the relative error of future scenarios of annual mean temperature by 16%. The HASM-based algorithm for spatial interpolation reduced the relative error of change trend of annual mean precipitation by 0.2%. The HASM-based algorithm for data fusion reduced the relative error of change trend of annual mean temperature by 70%. The HASM-based algorithm for model-data assimilation reduced the relative error of carbon stocks by 40%. We propose five theoretical challenges and three application problems of HASM that need to be addressed to improve FTEEM.

关键词： HASM FTEEM spatial upscaling spatial downscaling spatial interpolation Data fusion Model-data assimilation Model coupling

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LAND PRICE ASSESMENT BASED ON DEEP NEURAL NETWORK

LAND PRICE ASSESMENT BASED ON DEEP NEURAL NETWORK

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IEEE International geoscience and Remote Sensing Symposium

作者： Ankai Hou Jiaxi Liu Yuxuan Tao Shaobin Jiang Kai Li Zezhong Zheng Jun Xia Yong He Mingcang Zhu Guoqing Zhou Hongsheng Zhang Jiang Li School of Resources and Environment University of Electronic Science and Technology of China Chengdu PRC School of Resources and Environment University of Electronic Science and Technology of China Chengdu China State Key Laboratory of Water Resources and Hydropower Engineering Science Wuhan University Wuhan PRC Sichuan Research Institute for Eco-system Restoration & Geo-disaster Prevention Chengdu PRC Department of Natural Resources of Sichuan Province Chengdu PRC Guangxi Key Laboratory for Spatial Information and Geomatics Guilin University of Technology Guilin PRC Institute of Space and Earth Information Science The Chinese University of Hong Kong Shatin Hong Kong Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

The land resource is becoming scarcer and scarcer for a rapidly developing city. Thus, the land price assessment is important for the government to auction the land appropriately. In the paper, we introduced the deep neural network to evaluate the land price, taking the Shenzhen city in China as a case. Firstly, twenty influencing factors and land price data were gathered. Then, Shenzhen city was segmented into many grids with a size of 300 × 300 m. Secondly, the land price of each grid was derived with Kriging approach based upon the samples of land price. And the twenty influencing factors was quantified. Thirdly, the land price data and influencing factors were partitioned into training and testing datasets with the ratio of 8:1, and the training data were utilized to train the deep neural network based on regression analysis and classification with different hidden layers. Finally, the results were analyzed, and the deep neural network with the highest accuracy was selected as the optimum model. Therefore, our proposed method is an efficient approach to evaluate the land price with deep neural network.

关键词： Land price assessment Deep neural network

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URBAN FUNCTIONAL REGIONS DISCOVERING BASED ON DEEP LEARNING

URBAN FUNCTIONAL REGIONS DISCOVERING BASED ON DEEP LEARNING

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IEEE International geoscience and Remote Sensing Symposium

作者： Fan Mou Rui Kong Kai Li Zezhong Zheng Jun Xia Yong He Mingcang Zhu Guoqing Zhou Hongsheng Zhang Zhigang Liu Ankai Hou Ling Jiang Shengli Wang Jiang Li School of Resources and Environment University of Electronic Science and Technology of China Chengdu PRC State Key Laboratory of Water Resources and Hydropower Engineering Science Wuhan University Wuhan PRC Sichuan Research Institute for Eco-system Restoration & Geo-disaster Prevention Chengdu PRC Department of Natural Resources of Sichuan Province Chengdu PRC Guangxi Key Laboratory for Spatial Information and Geomatics Guilin University of Technology Guilin PRC Institute of Space and Earth Information Science The Chinese University of Hong Kong Shatin Hong Kong State Key Laboratory of Remote Sensing Science Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences Beijing PRC Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

In recent years, the big data industry chain has become more mature. Analyzing and managing cities by utilizing various big data in cities has become a hot research topic. Urban functional regions discovering is one of the important applications. The mainstream in urban functional regions discovering are probabilistic topic models, such as latent Dirichlet allocation (LDA) based topic model, which seeing the regions as documents and their functions are their topics. These methods require feature engineering by hand, which will construct features of limited expressiveness. To overcome these methods' shortcomings, we introduced a deep learning topic model called document neural autoregressive distribution estimation (DocNADE) into urban functional regions mining. And we did an experiment to test its effect. The experimental result shows that this DocNADE framework has achieved a considerable result in urban function inference compared with Dirichlet Multinomial Regression (DMR) based topic model which is a state of the art of urban functional regions discovering.

关键词： Biological system modeling Deep learning Probabilistic logic Roads Urban areas Laboratories Data mining

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Camouflaged Object Detection via Context-aware Cross-level Fusion

arXiv

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arXiv 2022年

作者： Chen, Geng Liu, Si-Jie Sun, Yu-Jia Ji, Ge-Peng Wu, Ya-Feng Zhou, Tao Northwestern Polytechnical University Xi’an710072 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering China Data Processing Center School of Power and Energy China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China School of Computer Science Inner Mongolia University Hohhot010021 China Artificial Intelligence Institute School of Computer Science Wuhan University Wuhan430072 China

Camouflaged object detection (COD) aims to identify the objects that conceal themselves in natural scenes. Accurate COD suffers from a number of challenges associated with low boundary contrast and the large variation of object appearances, e.g., object size and shape. To address these challenges, we propose a novel Context-aware Cross-level Fusion Network (C2F-Net), which fuses context-aware cross-level features for accurately identifying camouflaged objects. Specifically, we compute informative attention coefficients from multi-level features with our Attention-induced Cross-level Fusion Module (ACFM), which further integrates the features under the guidance of attention coefficients. We then propose a Dual-branch Global Context Module (DGCM) to refine the fused features for informative feature representations by exploiting rich global context information. Multiple ACFMs and DGCMs are integrated in a cascaded manner for generating a coarse prediction from high-level features. The coarse prediction acts as an attention map to refine the low-level features before passing them to our Camouflage Inference Module (CIM) to generate the final prediction. We perform extensive experiments on three widely used benchmark datasets and compare C2F-Net with state-of-the-art (SOTA) models. The results show that C2F-Net is an effective COD model and outperforms SOTA models remarkably. Further, an evaluation on polyp segmentation datasets demonstrates the promising potentials of our C2F-Net in COD downstream applications. Our code is publicly available at: https://***/Ben57882/C2FNet-TSCVT. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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