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IEEE International Geoscience and Remote Sensing Symposium

作者： Jiayin Liu Xiaolan Qiu Baoquan Zhang Feng Wang Lei Li Key Laboratory of Technology on Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Institute of Electronics Chinese Academy of Sciences Beijing China

The GF-3 satellite is the first full-polarization SAR satellite in China, which operates in the C band with a resolution of 1m. This paper presents a dual-aspect geometric terrain correction method to overcome the inherent shortages of SAR image such as foreshortening, shadow and layover. The results using GF-3 satellite data show that the method can effectively eliminate layover and shadow distortions in SAR images. This method solves the geometric correction problem that cannot be solved with a single SAR image.

关键词： Satellites Radar polarimetry Synthetic aperture radar Mathematical model Orbits Image resolution Satellite broadcasting

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The Challenges of LEO based Navigation Augmentation system - Lessons Learned from Luojia-1A satellite

The Challenges of LEO based Navigation Augmentation System -...

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第十届中国卫星导航学术年会

作者： Lei Wang Ruizhi Chen Beizhen Xu Xinxin Zhang Tao Li Cailun Wu State Key Laboratory of Satellite Navigation System and Equipment Technology Shijiazhuang China Collaborative Innovation Center for Geospatial Technology Wuhan 430079 China State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhan 430079 China

The Low earth orbiter(LEO) based navigation signal transmitters has advantages in fast-changing geometry and low free space loss, which can be served as a complementary or extension of current MEO/GEO based GNSS. Broadcasting navigation signals from LEO can significantly reduce the convergence time for the long baseline RTK\precise point positioning (PPP) and improve the signal strength. Hence LEO based navigation is considered as one of the key technology of next-generation positioning, navigation and timing (PNT) systems. This study assessed the pseudorange and carrier phase measurements observed from Luojia-1A satellite with the geometry-free combination method and the zero-baseline method. The assessment results indicate that the pseudorange precision variation subject to elevation angle is also caused by both signal strength variation and multipath effect. The pseudorange measurement precision reaches 0.7 m and 0.8 m respectively for the dual frequency signals and the carrier phase precision is 2.8 mm and 2.6 mm respectively. Based on the data collected from Luojia-1A satellite, the challenges of LEO navigation augmentation data processing were addressed. The most distinguished features of LEO navigation signals are their large signal strength variation, large Doppler variation and large acceleration variation. All these features have adversary effect on LEO signal processing and data processing, which has not been revealed. These challenges still need to be seriously investigated to further improve the performance of the LEO based navigation augmentation system.

关键词： Navigation Augmentation Signal Augmentation Low Earth Orbiters Luojia-1A

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Computation Offloading and Resource Allocation for MEC in C-RAN: A Deep Reinforcement Learning Approach 19

Computation Offloading and Resource Allocation for MEC in C-...

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19th IEEE International Conference on Communication technology, ICCT 2019

作者： Jin, Xiaoyan Zhang, Jun Sun, Xinghua Zhang, Ping Cai, Shu Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing210003 China Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education Nanjing University of Posts and Telecommunications Nanjing210003 China School of Electronics and Communication Engineering Sun Yat-sen University Guangzhou510006 China Key Laboratory of Wireless Sensor Network and Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China Key Laboratory of Computer Application Technology Anhui Polytechnic University Wuhu241000 China

ISBN: (纸本)9781728105352

Mobile edge computing (MEC) technology has become a promising example for cloud radio access networks (CRAN) to provide close-range services, thereby reducing service delays and saving energy consumption. In this paper, we consider a multi-user MEC system and solve the problem of the computation offloading strategies and resource allocation policies. We set the total cost of delays and energy consumption as our optimization goal. However, getting an optimal strategy in a dynamic environment is challenging. Reinforcement learning (RL) aims at long-term cumulative rewards, which are essential for time-varying dynamic systems. Therefore, we propose an optimization framework based on deep RL to solve these problems. The deep neural network (DNN) is used to estimate the value function of the critics, thereby reducing the state space complexity of the optimization target. The actor part uses another DNN to represent a parametritis stochastic strategy and improve the strategy with the help of critics. Compared with other schemes, the simulation results show that the scheme significantly reduces the total cost. © 2019 IEEE.

关键词： Reinforcement learning

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An Energy Efficient Carry-Free Inner Product Unit

An Energy Efficient Carry-Free Inner Product Unit

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

作者： Wen Yan Miloš D. Ercegovac Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing China University of California Los Angeles CA USA

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

An energy efficient truncated inner product unit is proposed in this paper. The proposed unit is pipelined and processes the m pairs of n-bit operands in serial, so that only one unit is required and it can be reused in each recurrence. Truncated inner product is produced so that no accumulation is needed for the lower part. The array reduction unit is bit-level pipelined to improve throughput and reduce the initial delay. A two-level [4:2] adder is proposed to accumulate redundant vectors with non-uniform input arrival. The proposed inner product is compared with other truncated inner product units using fully merged arithmetic, partially merged arithmetic and the counter based inner product for different precision and vector length. Our proposed inner product unit has significant reduction in area, power and energy.

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Progressive unsupervised person re-identification by tracklet association with spatio-temporal regularization

arXiv

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arXiv 2019年

作者： Xie, Qiaokang Zhou, Wengang Qi, Guo-Jun Tian, Qi 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 Hefei230027 China Huawei Cloud EI Product Department Huawei Noah's Ark Laboratory

Existing methods for person re-identification (Re-ID) are mostly based on supervised learning which requires numerous manually labeled samples across all camera views for training. Such a paradigm suffers the scalability issue since in real-world Re-ID application, it is difficult to exhaustively label abundant identities over multiple disjoint camera views. To this end, we propose a progressive deep learning method for unsupervised person Re-ID in the wild by Tracklet Association with Spatio-Temporal Regularization (TASTR). In our approach, we first collect tracklet data within each camera by automatic person detection and tracking. Then, an initial Re-ID model is trained based on within-camera triplet construction for person representation learning. After that, based on the person visual feature and spatio-temporal constraint, we associate cross-camera tracklets to generate cross-camera triplets and update the Re-ID model. Lastly, with the refined Re-ID model, better visual feature of person can be extracted, which further promote the association of cross-camera tracklets. The last two steps are iterated multiple times to progressively upgrade the Re-ID model. To facilitate the study, we have collected a new 4K UHD video dataset named Campus-4K with full frames and full spatio-temporal information. Experimental results show that with the spatio-temporal constraint in the training phase, the proposed approach outperforms the state-of-the-art unsupervised methods by notable margins on DukeMTMC-reID, and achieves competitive performance to fully supervised methods on both DukeMTMC-reID and Campus-4K datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Cameras

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Corrigendum to “Seasonal changes of leaf chlorophyll content as a proxy of photosynthetic capacity in winter wheat and paddy rice” [Ecol. Indic. 140 (2022) 109018]

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Ecological Indicators 2022年 141卷

作者： Jing Li Xuehe Lu Weimin Ju Ji Li Shihua Zhu Yanlian Zhou International Institute for Earth System Sciences Nanjing University Nanjing 210023 China School of Geography and Ocean Science Nanjing University Nanjing 210023 China School of Geography Science and Geomatics Engineering Suzhou University of Science and Technology Suzhou Jiangsu 215009 China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Jiangsu 210023 China Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application Nanjing Jiangsu 210023 China

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Waterline mapping of inland great lake with subpixel accuracy from GF-3 SAR images 6

Waterline mapping of inland great lake with subpixel accurac...

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

作者： Li, Ning Niu, Shilin Wang, Robert Wu, Lin Guo, Zhengwei College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Kaifeng475004 China Department of Space Microwave Remote Sensing System Institute of Electronics Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781728129129

High-accuracy waterline mapping with Synthetic Aperture Radar (SAR) images is a challenging task because of the inhomogeneities of SAR imagery caused by the speckle noise and complex terrain. This paper presents a novel method for waterline mapping with subpixel accuracy. The proposed method mainly consists of three steps. Firstly, an improved Non-Local filter is adapted to suppress the speckle noise. Secondly, a Fuzzy C-Means (FCM) clustering algorithm is used to extract the pixel-level waterline. Thirdly, along the pixel-level waterline, a novel subpixel-scale waterline extraction method based on bicubic convolution and Geometric Active Contour (GAC) model is presented to further improve the accuracy of the waterline. Without loss of generality, the Danjiangkou reservoir (DJKR), which is the largest artificial freshwater lake in Asia, is selected as a study case. SAR images from Chinese GaoFen-3 (GF-3) satellite working on different modes are processed to demonstrate the effectiveness of the proposed method. © 2019 IEEE.

关键词： Synthetic aperture radar

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A sub-pixel level map projection conversion method for high resolution of GF3 satellite products 12

A sub-pixel level map projection conversion method for high ...

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12th European Conference on Synthetic Aperture Radar, EUSAR 2018

作者： Wang, Wentao Liu, Jiayin Huang, Lijia Key Laboratory of Technology on Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783800746361

The GF-3 satellite launched in August 2016, is a C-band synthetic aperture radar(SAR) in China. With a resolution up to 1 m, GF-3 SAR imagery have made feasible the generation large-scale high resolution maps which need a high precision image registration technique. However, different remote sensing data usually have different geocoded formats. It causes misregistration when register SAR image with different projection formats. This paper discusses the method that convert the images from various projection coordinate to the unified geographic coordinate with a conversion accuracy of sub-pixel level. © VDE VERLAG GMBH • Berlin • Offenbach.

关键词： Synthetic aperture radar

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MRSEI LA : A modified remote sensing ecological index using local adaptability for enhancing ecological environment quality assessment

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Ecological Informatics 2025年 90卷

作者： Yingzhang Guo Mingjin Zhan Hanzeyu Xu Xiao Li Junjun Fang Xingchen Zhou Dan Lin Wenhui Chen Fujian Mapping Institute Fuzhou 350001 China School of Geographical Sciences School of Carbon Neutrality Future Technology Fujian Normal University Fuzhou 350117 China Jiangxi Vocational and Technical College of Information Application Nanchang 330043 China Jiangsu Key Laboratory of Crop Genetics and Physiology / Jiangsu Key Laboratory of Crop Cultivation and Physiology Agricultural College of Yangzhou University Yangzhou 225009 China Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops Yangzhou University Yangzhou 225009 China Research Institute of Smart Agriculture Yangzhou University Yangzhou 225009 China Transport Studies Unit University of Oxford Oxford OX1 3QY UK Linacre College University of Oxford Oxford OX1 3JA UK State Key Laboratory of Resource and Environmental Information System Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China University of Chinese Academy of Sciences Beijing 100049 China Liaoning Natural Resources Affairs Service Center Shenyang 110033 China

The Remote Sensing Ecological Index (RSEI) has been widely applied in ecological environment quality (EEQ) assessments by integrating multiple environmental factors. To enhance RSEI's ability to capture local ecological variations, a locally adapted version (RSEI LA ) was designed and widely adopted using moving windows. However, the randomness in eigenvector directions generated by principal component analysis (PCA) can introduce bias, affecting the accuracy of RSEI LA 's assessment. To enhance the effectiveness of RSEI LA in EEQ, we propose a modified RSEI LA model (MRSEI LA ) implemented on the Google Earth Engine (GEE) platform, consisting of three components: (1) optimization of moving window sizes tailored to each target region; (2) automatic recognition and correction of PCA-induced eigenvector direction inconsistencies; and (3) refinement of PCA computation within each circular window to improve the accuracy of EEQ evaluations. We validated MRSEI LA using Landsat Collection 2 Level-2 surface reflectance data and compared its performance with RSEI LA across four typical areas in China. The results showed that, compared to RSEI LA , MRSEI LA consistently produces aligned eigenvector directions and more accurate EEQ assessments that better reflect actual land surface conditions across all four testing areas, making it an effective tool for regional and large-scale ecological monitoring.

关键词： Ecological environment quality (EEQ) Remote sensing ecological index (RSEI) Moving window Principal component analysis (PCA) Eigenvector direction

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Transform-Invariant Convolutional Neural Networks for Image Classification and Search

arXiv

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arXiv 2019年

作者： Shen, Xu Tian, Xinmei He, Anfeng Sun, Shaoyan Tao, Dacheng CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei Anhui230027 China Centre for Quantum Computation & Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia

Convolutional neural networks (CNNs) have achieved state-of-the-art results on many visual recognition tasks. However, current CNN models still exhibit a poor ability to be invariant to spatial transformations of images. Intuitively, with sufficient layers and parameters, hierarchical combinations of convolution (matrix multiplication and nonlinear activation) and pooling operations should be able to learn a robust mapping from transformed input images to transform-invariant representations. In this paper, we propose randomly transforming (rotation, scale, and translation) feature maps of CNNs during the training stage. This prevents complex dependencies of specific rotation, scale, and translation levels of training images in CNN models. Rather, each convolutional kernel learns to detect a feature that is generally helpful for producing the transform-invariant answer given the combinatorially large variety of transform levels of its input feature maps. In this way, we do not require any extra training supervision or modification to the optimization process and training images. We show that random transformation provides significant improvements of CNNs on many benchmark tasks, including small-scale image recognition, large-scale image recognition, and image retrieval. The code is available at https: ACM ISBN 978-1-4503-2138-9. Copyright © 2019, The Authors. All rights reserved.

关键词： Image recognition

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