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arXiv 2020年

作者： Li, Xin Sun, Simeng Zhang, Zhizheng Chen, Zhibo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China

Versatile Video Coding (H.266/VVC) standard achieves better image quality when keeping the same bits than any other conventional image codec, such as BPG, JPEG, and etc. However, it is still attractive and challenging to improve the image quality with high compression ratio on the basis of traditional coding techniques. In this paper, we design the multi-scale grouped dense network (MSGDN) to further reduce the compression artifacts by combining the multi-scale and grouped dense block, which are integrated as the post-process network of VVC intra coding. Besides, to improve the subjective quality of compressed image, we also present a generative adversarial network (MSGDN-GAN) by utilizing our MSGDN as generator. Across the extensive experiments on validation set, our MSGDN trained by MSE losses yields the PSNR of 32.622 on average with teams"IMC" and"haha" at the bit-rate of 0.15 in Low-rate track. Moreover, our MSGDN-GAN could achieve the better subjective performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Image quality

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Spatial distribution of rare earth elements and their impact factors in an area with a high abundance of regolith-hosted deposits

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Chemosphere 2024年 352卷 141374页

作者： Lin, Zhuoling Zhang, Yaduo Liang, Xiaoliang Huang, Guangqing Fan, Fenglei Yin, Xiaoling Chen, Zhihao Guangzhou Institute of Geochemistry Chinese Academy of Sciences Guangzhou 510640 China Guangdong Public Laboratory of Geospatial Information Technology and Application Guangzhou Institute of Geography Guangdong Academy of Sciences Guangzhou 510070 China University of Chinese Academy of Sciences Beijing 100049 China School of Geography South China Normal University Guangzhou 510631 China Guangdong Provincial Key Laboratory of Mineral Physics and Material Research & Development Guangzhou 510640 China College of Natural Resources and Environment South China Agricultural University Guangzhou 510642 China

Despite the widespread occurrence of regolith-hosted rare earth elements (REEs) across South China, their spatial distribution characteristics in soils and their impact factors remain largely uncertain. This knowledge gap impedes the exploration of regolith-hosted REE deposits and the assessment of the environmental risks associated with REEs. To address this issue, 180 soil samples were collected from Meizhou City, Guangdong Province, a region known for its high abundance of regolith-hosted REEs. Subsequently, the correlations between REE enrichment/fractionation and various factors, i.e., topography, climate conditions, land use, and landform were analysed using the geo-detector method. The results revealed a highly uneven spatial distribution of REEs and their fractionation features with some regions displaying distinct spatial patterns. Elevation was the dominant factor influencing this distribution, and showed strong correlations with the concentrations of REEs, light REEs (LREEs) and heavy REEs (HREEs);the LREE/HREE ratio;and the positive Ce anomaly (δCe). The negative Eu anomaly (δEu) showed a good correlation with rock type. The enrichment and fractionation of REEs indicated a coupling among the abovementioned factors. For REE enrichment, areas with elevations of 138–148 m, precipitation levels of 1553–1574 mm, annual average land surface temperatures of 30.4–30.5 °C, leaf area index values of 22–29 and surface cutting degree of 21.5–29.9 m showed the highest average abundance within each type (scope) of the predominant factors. These findings highlight the key factors affecting REE distribution, thereby aiding the efficient utilization of regolith-hosted REE resources and the evaluation of their environmental risks. © 2024 Elsevier Ltd

关键词： Geo-detector REE enrichment REE fractionation Soil Spatial heterogeneity Topography

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Texture-enhanced Light Field Super-resolution with Spatio-Angular Decomposition Kernels

arXiv

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arXiv 2021年

作者： Hu, Zexi Chen, Xiaoming Wing, Henry Yeung, Fung Chung, Yuk Ying Chen, Zhibo School of Computer Science University of Sydney Australia School of Computer Science and Engineering Beijing Technology and Business University China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China China

Despite the recent progress in light field super-resolution (LFSR) achieved by convolutional neural networks, the correlation information of light field (LF) images has not been sufficiently studied and exploited due to the complexity of 4D LF data. To cope with such high-dimensional LF data, most of the existing LFSR methods resorted to decomposing it into lower dimensions and subsequently performing optimization on the decomposed sub-spaces. However, these methods are inherently limited as they neglected the characteristics of the decomposition operations and only utilized a limited set of LF sub-spaces ending up failing to sufficiently extract spatio-angular features and leading to a performance bottleneck. To overcome these limitations, in this paper, we comprehensively discover the potentials of LF decomposition and propose a novel concept of decomposition kernels. In particular, we systematically unify the decomposition operations of various sub-spaces into a series of such decomposition kernels, which are incorporated into our proposed Decomposition Kernel Network (DKNet) for comprehensive spatio-angular feature extraction. The proposed DKNet is experimentally verified to achieve considerable improvements compared with the state-of-the-art methods. To further improve DKNet in producing more visually pleasing LFSR results, based on the VGG network, we propose a LFVGG loss to guide the Texture-Enhanced DKNet (TE-DKNet) to generate rich authentic textures and enhance LF images’ visual quality significantly. We also propose an indirect evaluation metric by taking advantage of LF material recognition to objectively assess the perceptual enhancement brought by the LFVGG loss. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical resolving power

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MSight: An Edge-Cloud Infrastructure-based Perception system for Connected Automated Vehicles

arXiv

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arXiv 2023年

作者： Zhang, Rusheng Meng, Depu Shen, Shengyin Zou, Zhengxia Li, Houqiang Liu, Henry X. The Department of Civil and Environmental Engineering University of Michigan Ann ArborMI48109 United States The University of Michigan Transportation Research Institude 2901 Baxer Rd Ann ArborMI48109 United States The Department of Guidance Navigation and Control School of Astronautics Beihang University Beijing100191 China The 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 Mcity University of Michigan Ann ArborMI48109 United States

As vehicular communication and networking technologies continue to advance, infrastructure-based roadside perception emerges as a pivotal tool for connected automated vehicle (CAV) applications. Due to their elevated positioning, roadside sensors, including cameras and lidars, often enjoy unobstructed views with diminished object occlusion. This provides them a distinct advantage over onboard perception, enabling more robust and accurate detection of road objects. This paper presents MSight, a cutting-edge roadside perception system specifically designed for CAVs. MSight offers real-time vehicle detection, localization, tracking, and short-term trajectory prediction. Evaluations underscore the system’s capability to uphold lane-level accuracy with minimal latency, revealing a range of potential applications to enhance CAV safety and efficiency. Presently, MSight operates 24/7 at a two-lane roundabout in the City of Ann Arbor, Michigan. Copyright © 2023, The Authors. All rights reserved.

关键词： Roadsides

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Multi-Aspect SAR Target Recognition Based on Efficientnet and GRU

Multi-Aspect SAR Target Recognition Based on Efficientnet an...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Pengfei Zhao Lijia Huang Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese of Sciences Beijing China

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Automatic target recognition (ATR) in synthetic aperture radar (SAR) has been extensively applied in military and civilian fields recently. However, SAR images are very sensitive to the azimuth of the imaging, and the same target at different aspects differs greatly, thus requiring more reliable and robust multi-aspect ATR recognition performance. In this paper, we propose an end-to-end multi-aspect ATR model based on EfficientNet and GRU, and use island loss as the training loss, which is more suitable for SAR ATR. Experiments show that our proposed method can achieve 100% accuracy for 10-class recognition, and 99.68% for a large depression angle. Besides, the proposed method can achieve satisfactory accuracy even with reduced datasets. Experimental results have shown that our proposed method outperform other state-of-the-art ATR methods.

关键词： Training Synthetic aperture radar Feature extraction Radar polarimetry Target recognition Data models Image sequences

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Sea-water-level prediction via combined wavelet decomposition,neuro-fuzzy and neural networks using SLA and wind information

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Acta Oceanologica Sinica 2020年第5期39卷 157-167页

作者： Bao Wang Bin Wang Wenzhou Wu Changbai Xi Jiechen Wang School of Geography and Ocean Science Nanjing UniversityNanjing 210023China National Marine Environmental Forecasting Center Beijing 100081China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of SciencesBeijing 100101China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Nanjing UniversityNanjing 210023China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing UniversityNanjing 210023China

Sea-water-level(SWL)prediction significantly impacts human lives and maritime activities in coastal regions,particularly at offshore locations with shallow water ***-term SWL forecasts,which are conventionally obtained via harmonic analysis,become ineffective when nonperiodic meteorological events *** intelligence combined with other data-processing methods can effectively forecast highly nonlinear and nonstationary inflow patterns by recognizing historical relationships between input and *** techniques are considerably useful in time-series data *** paper reports the development of a hybrid model to realize accurate multihour SWL forecasting by combining an adaptive neuro-fuzzy inference system(ANFIS)with wavelet decomposition while using sea-level anomaly(SLA)and wind-shear-velocity components as *** wavelet-ANFIS(WANFIS)models have been tested using different inputs to assess their applicability as alternatives to the artificial neural network(ANN),wavelet ANN(WANN),and ANFIS *** error definitions have been used to evaluate results,which indicate that integrated wavelet-decomposition and ANFIS models improve the accuracy of SWL prediction and that the inputs of SLA and wind-shear velocity exhibit superior prediction capability compared to conventional SWL-only models.

关键词： sea-water level prediction ANFIS wavelet decomposition wind

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Spatial Distribution of Rare Earth Elements and Their Impact Factors in an Area with a High Abundance of Regolith-Hosted Deposits

SSRN

引用

SSRN 2023年

作者： Lin, Zhuoling Zhang, Yaduo Liang, Xiaoliang Huang, Guangqing Fan, Fenglei Yin, Xiaoling Chen, Zhihao Guangzhou Institute of Geochemistry Chinese Academy of Sciences Guangzhou510640 China Guangdong Public Laboratory of Geospatial Information Technology and Application Guangzhou Institute of Geography Guangdong Academy of Sciences Guangzhou510070 China School of Geography South China Normal University Guangzhou510631 China University of Chinese Academy of Sciences Beijing100049 China Guangdong Provincial Key Laboratory of Mineral Physics and Material Research & Development Guangzhou510640 China College of Natural Resources and Environment South China Agricultural University Guangzhou510642 China

Rare earth elements (REEs) are critical metals for manufacturing numerous high-tech products, but at high concentrations, pose potential hazards owing to their toxicity to animals and human health. Despite the widespread occurrence of regolith-hosted REEs across South China, their spatial distribution characteristics in soils and attendant influencing factors remain largely unknown. This knowledge gap impedes the exploration of regolith-hosted REE deposits and the assessment of the environmental risks associated with REEs. To address this issue, 180 soil samples were collected from Meizhou City, Guangdong Province, a region known for its high abundance of regolith-hosted REEs. Subsequently, the correlations between REE enrichment/fractionation and various factors, i.e., topography, climate conditions, land use, and landform were analysed using the geo-detector method. The results revealed a highly uneven spatial distribution of REEs and their fractionation features with some regions displaying distinct spatial patterns. Elevation was the dominant factor influencing this distribution, and showed strong correlations with the concentrations of REEs, light REEs (LREEs) and heavy REEs (HREEs);the LREE/HREE ratio;and the positive Ce anomaly (δCe). The negative Eu anomaly (δEu) showed a good correlation with rock type. The enrichment and fractionation of REEs indicated a coupling among the abovementioned factors. The crucial scopes of the predominant factors were identified using a risk detector. For REE enrichment, areas with elevations of 137–148 m, precipitation levels of 1553–1574 mm, annual average land surface temperatures of 30.4–30.5 °C, leaf area index values of 22–29 and relief degrees of land surface of 21.4–29.9 m showed the highest average abundance within each type (scope) of the predominant factors. These findings highlight the key factors affecting REE distribution, thereby aiding the efficient utilization of regolith-hosted REE resources and the assessment

关键词： Soils

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Automatic Extraction of Supraglacial Lake using SAR Imagery and Deep Learning

Automatic Extraction of Supraglacial Lake using SAR Imagery ...

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IEEE International Conference on Radar

作者： Jiang Di Li Xinwu Gong Chen Hong Wen Wu Yirong Aerospace Information Research Institute Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665468893

Supraglacial lake plays an important role in ice sheet dynamics, mass balance and sea level rise. Therefore, it is of great importance to extract supraglacial lake and obtain its spatial-temporal distribution or change. This study provides an automatic extraction model for supraglacial lake, using Synthetic Aperture Radar (SAR) imagery based on deep learning. First, select 19580 Sentinel-1 SAR imagery patches in eight typical areas for manual labeling. Second, the GPU-based U-Net model is used to implement the training of the supraglacial lake, and the results are evaluated in different sites. Finally, the training model is used to perform the supraglacial lake extraction. In addition, this article also introduces ArcticDEM to remove shadow confusion in the margin of the ice sheet. The global-local threshold segmentation method is used to extract the supraglacial lake on the Sentinel-2 MSI imagery, which is a comparative analysis and information supplement for the extracted results in this paper. The results show that: (1) The U-Net network selected in this paper is suitable for processing small sample size SAR data and multi-modal feature extraction. The GPU parallel processing method can achieve rapid extraction of massive data and reduce time cost. (2) The Dice coefficient of the training model reaches 0.98, which can be used for effective extraction of supraglacial lake. (3) Compared with the results of optical image extraction, the algorithm proposed in this paper can identify lakes in areas covered by snow or thin ice, which truly reflects the supraglacial lake temporal and spatial distribution characteristics.

关键词： Training Deep learning Snow Lakes Feature extraction Prediction algorithms Radar polarimetry

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High-precision inversion of vegetation parameters in the AI era: Integrating hyperspectral remote sensing and deep learning

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Innovation 2025年第6期6卷

作者： Hu, Jinkang Peng, Dailiang Chen, Jing M. Huete, Alfredo R. Yu, Le Lou, Zihang Cheng, Enhui Yang, Xuan Zhang, Bing Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China International Research Center of Big Data for Sustainable Development Goals Beijing100094 China College of Resource and Environment University of Chinese Academy of Sciences Beijing100094 China School of Geographical Sciences Fujian Normal University Fuzhou350117 China Department of Geography and Planning University of Toronto TorontoONM5S 3G3 Canada School of Life Sciences University of Technology Sydney UltimoNSW2007 Australia Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing100084 China Institute of Agricultural Remote Sensing and Information Technology Application College of Environmental and Resource Sciences Zhejiang University Hangzhou310058 China China Remote Sensing Satellite Ground Station Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China

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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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