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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Feng, Aolin Gao, Changsheng Li, Li Liu, Dong Wu, Feng CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

ISBN: (纸本)9781665438643

High Efficiency Video Coding (HEVC) achieves significant improvement in compression efficiency by introducing quadtree-based block partition. However, in the HEVC reference software-HM, the optimal partition is found by a recursive rate-distortion optimization (RDO) process, which is computationally expensive and not friendly to hardware implementation. We propose a fast block partitioning algorithm using convolutional neural network (CNN) based depth map prediction for HEVC intra coding. We use the depth map to represent the block partition of a coding tree unit (CTU). Then, we design a CNN to predict the depth map for a CTU, and we construct a large-scale dataset to train the CNN. Through the depth map prediction, we obtain a block partitioning structure for the entire CTU, and then we could directly compress each coding unit, getting rid of the recursive RDO process for partitioning. Experimental results show that our proposed method reduces 65.55% encoding time of HM at the cost of 2.02% Bjøntegaard Delta rate (BD-rate) increase on the common test sequences. For 4K sequences, our method achieves 76.97% time saving with 2.89% BD-rate increase. © 2021 IEEE

关键词： Forecasting

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L-hypersurface Based Parameters Selection for Sparse SAR Imaging via Composite Regularization

L-hypersurface Based Parameters Selection for Sparse SAR Ima...

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2022 International Conference on Radar Systems, RADAR 2022

作者： Fan, Yizhe Xu, Zhongqiu Zhou, Guoru Zhang, Bingchen Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences China Key Laboratory of Technology in Geo-spatial Information Processing and Application System School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781839537776

Composite regularization models are widely used in sparse signal processing, making multiple regularization parameters selection a significant problem to be solved. Variety kinds of composite regularization models are used in SAR imaging, including L1 and TV penalty, L1 and L2,1 penalty, etc. In this article, a new adaptive multiple regularization parameters selection method named L-hypersurface is proposed. The effectiveness of the proposed method is verified by experiments. Simulation experiments indicate that the selected optimal regularization parameters have satisfied reconstruction results, both visually and numerically. Furthermore, experiments on Gaofen-3 SAR satellite data are also exploited to show the performance of the proposed method. © IET Conference Proceedings. All rights reserved.

关键词： Synthetic aperture radar

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Object Segmentation-Assisted Inter Prediction for Versatile Video Coding

arXiv

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arXiv 2024年

作者： Li, Zhuoyuan Yuan, Zikun Li, Li Liu, Dong Tang, Xiaohu Wu, Feng CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China Information Security and National Computing Grid Laboratory Southwest Jiaotong University Chengdu610031 China

In modern video coding standards, block-based inter prediction is widely adopted, which brings high compression efficiency. However, in natural videos, there are usually multiple moving objects of arbitrary shapes, resulting in complex motion fields that are difficult to represent compactly. This problem has been tackled by more flexible block partitioning methods in the Versatile Video Coding (VVC) standard, but the more flexible partitions require more overhead bits to signal and still cannot be made arbitrarily shaped. To address this limitation, we propose an object segmentation-assisted inter prediction method (SAIP), where objects in the reference frames are segmented by some advanced technologies. With a proper indication, the object segmentation mask is translated from the reference frame to the current frame as the arbitrary-shaped partition of different regions without any extra signal. Using the segmentation mask, motion compensation is separately performed for different regions, achieving higher prediction accuracy. The segmentation mask is further used to code the motion vectors of different regions more efficiently. Moreover, the segmentation mask is considered in the joint rate-distortion optimization for motion estimation and partition estimation to derive the motion vector of different regions and partition more accurately. The proposed method is implemented into the VVC reference software, VTM version 12.0. Experimental results show that the proposed method achieves up to 1.98%, 1.14%, 0.79%, and on average 0.82%, 0.49%, 0.37% BD-rate reduction for common test sequences, under the Low-delay P, Low-delay B, and Random Access configurations, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Video signal processing

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Offline and Online Optical Flow Enhancement for Deep Video Compression

arXiv

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arXiv 2023年

作者： Tang, Chuanbo Sheng, Xihua Li, Zhuoyuan Zhang, Haotian Li, Li Liu, Dong 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

Video compression relies heavily on exploiting the temporal redundancy between video frames, which is usually achieved by estimating and using the motion information. The motion information is represented as optical flows in most of the existing deep video compression networks. Indeed, these networks often adopt pre-trained optical flow estimation networks for motion estimation. The optical flows, however, may be less suitable for video compression due to the following two factors. First, the optical flow estimation networks were trained to perform inter-frame prediction as accurately as possible, but the optical flows themselves may cost too many bits to encode. Second, the optical flow estimation networks were trained on synthetic data, and may not generalize well enough to real-world videos. We address the twofold limitations by enhancing the optical flows in two stages: offline and online. In the offline stage, we fine-tune a trained optical flow estimation network with the motion information provided by a traditional (non-deep) video compression scheme, e.g. H.266/VVC, as we believe the motion information of H.266/VVC achieves a better rate-distortion tradeoff. In the online stage, we further optimize the latent features of the optical flows with a gradient descent-based algorithm for the video to be compressed, so as to enhance the adaptivity of the optical flows. We conduct experiments on a state-of-the-art deep video compression scheme, DCVC. Experimental results demonstrate that the proposed offline and online enhancement together achieves on average 12.8% bitrate saving on the tested videos, without increasing the model or computational complexity of the decoder side. Copyright © 2023, The Authors. All rights reserved.

关键词： Image coding

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Visualization and visual analysis of cyberspace metaphors based on continuous cartogram 2

Visualization and visual analysis of cyberspace metaphors ba...

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2023 2nd International Conference on Environmental Remote Sensing and geographic information technology, ERSGIT 2023

作者： Liu, Longhui Zhou, Yang Shi, Qunshan Hu, Xiaofei Shi, Shihao PLA Strategic Support Force Information Engineering University Institute of Geospatial Information Henan Zhengzhou450001 China Collaborative Innovation Center of Geo-Information Technology for Smart Central Plains Zhengzhou450001 China Key Laboratory of Spatiotemporal Perception and Intelligent Processing Ministry of Natural Resources Zhengzhou450001 China

ISBN: (纸本)9781510672949

Cyberspace mapping has emerged as a new and compelling area of research within the field of cartography. This paper addresses the challenge of overlapping points in cyberspace visualization, which poses difficulties for visual analysis. To tackle this issue, we propose a cyberspace metaphorical cartogram map that combines metaphorical mapping and continuous cartogram technology. Additionally, based on our method, we use our method to construct a cyberspace map using global IPv6 data from the Asia-Pacific Network information Center (APNIC) to analyze the current development pattern of the global IPv6 network. Experimental results demonstrate the effectiveness of our method in avoiding node duplication, aligning with the reading logic of geospatial maps, and providing an efficient visualization approach for data comprehension. © 2024 SPIE.

关键词： Visualization

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GRIDLESS DOA ESTIMATION FOR AUTOMOTIVE RADARS WITH VARIOUS ARRAY geoMETRIES: THE NON-VANDERMONDE ATOMIC SOFT THRESHOLDING APPROACH

GRIDLESS DOA ESTIMATION FOR AUTOMOTIVE RADARS WITH VARIOUS A...

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IET International Radar Conference 2023, IRC 2023

作者： Gao, Silin Wang, Muhan Zhang, Zhe Zhang, Bingchen Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China

ISBN: (纸本)9781839539954

This paper is centered on gridless direction of arrival (DoA) estimation for single-snapshot data collected by non-uniform linear arrays (NLAs) in the context of automotive applications. While recent single-snapshot DoA estimation algorithms rooted in grid-based compressed sensing (CS) offer super-resolution capabilities, they are notably sensitive to discrepancies between the assumed sparsity basis and the actual basis. Furthermore, the existing atomic soft thresholding (AST) algorithm, a promising gridless sparse recovery technique within the Toeplitz model, is constrained to uniform linear arrays (ULAs) exhibiting Vandermonde structure in their array manifolds. However, in automotive scenarios, it becomes imperative to efficiently apply gridless DoA estimation to NLAs featuring non-Vandermonde array manifolds. In this paper, we introduce the non-Vandermonde atomic soft thresholding (NVAST) algorithm and employ it for DoA estimation in NLAs. The novel algorithm establishes a link between Vandermonde and non-Vandermonde atoms by decomposing non-Vandermonde vectors and addressing them using semi-definite programming (SDP). Simulation and measurement experiments conducted with automotive radars underscore the remarkable performance of our proposed method. © The Institution of Engineering & technology 2023.

关键词： Automotive radar

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AN AUTOFOCUS NETWORK FOR MULTI-CHANNEL PHASE ERRORS WITH APPLICATION TO TOMOSAR IMAGING

AN AUTOFOCUS NETWORK FOR MULTI-CHANNEL PHASE ERRORS WITH APP...

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IET International Radar Conference 2023, IRC 2023

作者： Wang, Muhan Gao, Silin Zhang, Zhe Qiu, Xiaolan Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781839539954

Synthetic aperture radar (SAR) tomography (TomoSAR) has garnered significant attention due to its capability for three-dimensional reconstruction. Compressed sensing (CS) methods are widely employed to address the TomoSAR inversion challenge. Nevertheless, practical applications reveal phase errors among different channels, resulting in defocusing and blurring when relying solely on CS for 3D reconstruction. Current state-of-the-art autofocus techniques suffer from prohibitive computational complexity, limiting their applicability to large-scale 3D imaging. In pursuit of efficient TomoSAR 3-D autofocusing, we proposed ASAMP-Net, an innovative deep unfolding network. Operating within a two-step framework, each layer comprises two stages: phase error estimation and iterative scattering coefficient reconstruction using the sparse adaptive matching pursuit (SAMP) algorithm. Additionally, phase error estimation is obtained through mathematical derivation, while challenges associated with fixed sparsity and limited efficiency in conventional methods are mitigated through deep learning techniques. Simulation experiments and real data validation affirm the effectiveness and superiority of the proposed method. © The Institution of Engineering & technology 2023.

关键词： Radar imaging

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A novel methodology for calculating the aerosol direct radiative effect using a non-uniform five-dimensional lookup table (Dec, 10.1080/01431161.2023.2295833, 2023)

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2024年第3期45卷 0i-0i页

作者： Shi, Zhenwei Xu, Ning Li, Jianzhong Zhang, Zhaoxu Zhao, Hui Longa, Hui Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing China School of Environmental Science and Engineering Tiangong University Tianjin China Surveying and Mapping Geographic Information Center Geological Brigade of Shandong Nuclear Industry Qingdao Shandong China

The quantification of aerosol direct radiative effects (ADREs) under clear sky conditions is essential for understanding the influence of aerosols on climate. This paper introduces a novel approach, the non-uniform five-dimensional lookup table (NU-5D-LUT) method, for calculating the ADREs under clear sky conditions. The NU-5D-LUT method considers key parameters such as aerosol optical thickness, single scattering albedo, asymmetry factor, surface albedo, and solar zenith angle. Validation against the SBDART model shows strong agreement, with correlation coefficients of 0.97 and 0.99 for TOA and BOA ADREs, respectively. Global comparisons demonstrate consistent ADRE distributions, with relative errors below 5.8% for TOA ADRE and 4.4% for BOA ADRE. The NU-5D-LUT method significantly reduces computation time, offering a practical solution for large-scale simulations and climate model evaluations in aerosol research.

关键词： Aerosol direct radiative effect aerosol optical properties lookup table

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Monitoring Changes to Small-Sized Lakes Using Medium spatial and Temporal Satellite Imagery in the Badain Jaran Desert from 2015 to 2020

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International Journal of Crowd Science 2025年第2期9卷 96-109页

作者： Qinyu Zhao Luyan Ji Yonggang Su Kai Yu Yongchao Zhao College of Electronic and Information Engineering Hebei University Baoding China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-Spatial information Processing and Application System Beijing China Key Laboratory of Target Cognition and Application Technology (TCAT) Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

The Badain Jaran Desert is the second-largest desert in China, and its lakes, which are generally small-sized and highly dynamic, play a significant role for plants and animals in this arid region. Therefore, long-term monitoring of the distribution of lakes in the Badain Jaran Desert with high spatial and temporal resolution is of great importance. However, due to the tradeoff between pixel size and swath width, currently no single satellite sensor can provide such a time series. Thereby, in this study, we focus on applying the deep learning based spatiotemporal fusion method (super-resolution based spatial fusion with Generative Adversarial Network (GAN)) to a low spatial yet high temporal resolution data (i.e., MODIS 250 m daily reflectance time series) and a high spatial yet low temporal resolution data (i.e., Landsat 30 m 16-day reflectance time series) to generate a daily 30 m time series for 37 selected lakes in the Badain Jaran Desert. Then, an automatic water extraction algorithm is proposed, and a daily 30 m water mapping production is generated for our study area from 2015 to 2020. The overall accuracy can reach 0.92, while the average error of lake areas is less than 9.21%, which is much higher than that derived from the MODIS time series. Moreover, based on our daily high spatial resolution results, it is possible to analyze the water phenology for all sizes of lakes in the Badain Jaran Desert. We have performed a detailed analysis of interannual variability and seasonal changes for the selected 37 lakes in the Badain Jaran Desert. The results show that from 2015 to 2020, the shrinkage of the small lakes (<0.5 km 2 ) is more severe than lakes with a larger size. As for seasonal changes, the lake area can be divided into four stages: quick increase due to ice melting from winter to spring, slow decrease due to evaporation from spring to summer, moderate recovery due to the arrival of the rainy season from summer to autumn, and quick decrease due to lake

关键词： Reflectivity Heuristic algorithms Time series analysis Landsat Lakes Deserts Spatiotemporal phenomena spatial resolution MODIS Monitoring

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Active Learning with Core-Set Sampling and Scale-Sensitive Loss for 3D Object Detection

Active Learning with Core-Set Sampling and Scale-Sensitive L...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Dejun Zhang Xiaowei Lin Benxin Yi Yiqi Wu Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Information Systems Technology and Design Pillar Singapore University of Technology and Design

Deep learning-based 3D object detectors often require large-scale labeled 3D datasets, which can be expensive to annotate. To tackle this issue, we introduce a core-set sampling strategy within an active learning framework, selecting highly informative data from a data pool to reduce reliance on such datasets. Additionally, we introduce a scale-sensitive loss function into the 3D object detector to mitigate the disparities in the influence of small and large objects on model learning, thereby enhancing the accuracy of small object detection. Our experiments on the KITTI dataset demonstrate that our method achieves comparable results to the baseline using only 20% of the data. Notably, our approach outperforms the baseline in small object detection, with an 8% accuracy improvement for pedestrians and a 4% improvement for cyclists. The source code of the proposed method is available at https://***/djzgroup/al-cs-ssl.

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