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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Hongze Dong Xinye Tang Mingcang Zhu Guoqing Zhou Zezhong Zheng Xuefeng Yang School of Resources and Environment University of Electronic Science and Technology of China Chengdu PRC Sichuan Research Institute for Eco-System Restoration & Geo-Hazard 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 China Railway Eryuan Engineering Group Co. LTD Chengdu PRC

Landslide natural disasters (LND) have high frequency, wide distribution, and multiple occurrences, causing significant losses to personal and property safety. LNDs account for over 70% of natural geological disasters in China, often caused by precipitation. Xingguo County, Jiangxi Province, is prone to LND due to its geographical location. Rainfall-induced LNDs account for over 70% of the county's LND. In this study, a digital modeling and machine learning approach is used to evaluate the susceptibility of rain-induced landslides in Xingguo County and generate a high-precision susceptibility map. Six influence factors are selected, and four machine learning algorithms, including support vector machine (SVM), decision tree (DT), back propagation neural network (BPNN), and random forests (RF), are used for susceptibility evaluation. A rainfall-induced landslide susceptibility map is derived, and landslide points are classified into five susceptive types. The experimental results show that the BPNN model achieved the best performance. The accuracy of the models is validated using the area under the receiver operating characteristic curve (ROC), area under the curve (AUC), accuracy (ACC), and kappa coefficient. The results showed that all models performed well, but the BPNN model achieved the best performance with an AUC of 0.75, ACC of 0.67, and kappa coefficient of 0.75.

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Recommendation of Landslide Treatment Measures Based on Random Forest

Recommendation of Landslide Treatment Measures Based on Rand...

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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Maosheng Lina Xinglong Liua Mingcang Zhub Guoqing Zhouc Zezhong Zheng Zhanyong Hed Shuang Yua Xuefeng Yange School of Resources and Environment University of Electronic Science and Technology of China 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 Sichuan Research Institute for Eco-System Restoration & Geo-Hazard Prevention Chengdu PRC China Railway Eryuan Engineering Group Co. LTD Chengdu

Landslide is one of the major geological disasters in China, which brings huge economic losses to our people every year. However, in the field of landslide treatment, the application of machine learning is scarce. In order to fill the gap in the field of landslide treatment measures based on machine learning. Firstly, random forest classification or regression algorithm was used to train and forecast each landslide treatment measure in this paper. Accuracy (ACC) was used to test the model accuracy of classification algorithm, and Mean Absolute Error (MAE) is used to test the model accuracy of regression algorithm. Random forest classification algorithm was adopted for non-numerical measures. And random forest regression algorithm was adopted for the numerical treatment measures. Secondly, the feature importance of the random forest model was calculated to obtain the more important features of each landslide treatment measure in this paper. Based on this, an optimized random forest model was constructed, and finally the optimal random forest regression and classification algorithm model suitable for landslide treatment measures recommendation was obtained. The training data dimensions of the model were reduced from 58 dimensions to 4-10 dimensions. The experimental results showed that our model could greatly improve the accuracy.

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MF-JMoDL-Net: A Deep Network for Azimuth Undersampling Pattern Design and Ambiguity Suppression for Sparse SAR Imaging

arXiv

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arXiv 2023年

作者： Wu, Yuwei Zhang, Zhe Qiu, Xiaolan Zhao, Yao Yu, Weidong Department of Space Microwave Remote Sensing System Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Jiangsu Suzhou China Suzhou Aerospace Information Research Institute Jiangsu Suzhou China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Guangdong University of Technology Guangdong Guangzhou China

Traditionally, the range swath of a synthetic aperture radar (SAR) system is constrained by its pulse repetition frequency (PRF). Given the system complexity and resource constraints, it is often difficult to achieve high imaging performance and low ambiguity without compromising the swath. In this paper, we propose a joint optimization framework for sparse strip SAR imaging algorithms and azimuth undersampling patterns based on a deep convolutional neural network, combined with matched filter (MF) approximate measurement operators and inverse MF operators, referred to as MF-JMoDL-Net, for sparse SAR imaging methods. Compared with conventional sparse SAR imaging, MF-JMoDL-Net enables us to alleviate the limitations imposed by PRF. In the proposed scheme, joint and continuous optimization of azimuth undersampling patterns and convolutional neural network parameters are implemented to suppress azimuth ambiguity and enhance sparse SAR imaging quality. Experiments and comparisons under various conditions demonstrate the effectiveness and superiority of the proposed framework in imaging results. Copyright © 2023, The Authors. All rights reserved.

关键词： Matched filters

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Characterizing key Foliar Functional Traits of Subtropical Evergreen Forests in South China Using Leaf and Uav-Based Spectroscopy

SSRN

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SSRN 2023年

作者： Wang, Zhihui Sun, Zhongyu Liu, Nanfeng Geng, Shoubao Wen, Meili Zhang, Hui Yang, Long Guangdong Provincial Key Laboratory of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou Institute of Geography Guangdong Academy of Sciences Guangzhou510070 China Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation School of Geography and Planning Sun Yat-Sen University Guangzhou510275 China College of Forestry Wuzhishan National Long-Term Forest Ecosystem Monitoring Research Station Hainan University Haikou570228 China Ministry of Education College of Forestry Hainan University Haikou570228 China

Spectroscopy provides an efficient way of predicting foliar functional traits at both leaf and canopy scales. Previous studies mainly used hyperspectral data from airborne and spaceborne platforms, and few studies assessed the feasibility of the more flexible unmanned airborne vehicle (UAV) -based imaging spectroscopy in subtropical evergreen forests. This study aims to characterize key foliar traits of subtropical evergreen forests in South China using leaf and UAV-based spectroscopy. Dry and fresh leaf spectra as well as key foliar traits (leaf chlorophyll and carotenoids, water, dry matter, nitrogen, phosphorus, and potassium) were collected from 360 samples over 97 species across four field sites. For the site Heshan, we also collected UAV-based hyperspectral imageries at two dates. Partial least squares regression (PLSR) and random forest regression (RF) were used to link foliar traits with dry leaf spectra, fresh leaf spectra and canopy spectra, respectively. Cross-site and cross-season validations were performed to assess model transferability. Results showed that dry and fresh leaf spectra could estimate most foliar traits with comparable accuracies (normalized RMSE (NRMSE) © 2023, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Inference of Urban Function Zone Based on Deep Neural Network

Inference of Urban Function Zone Based on Deep Neural Networ...

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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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