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arXiv 2024年

作者： Wang, Henan Zhu, Hanxin Chen, Zhibo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

Light field, as a new data representation format in multimedia, has the ability to capture both intensity and direction of light rays. However, the additional angular information also brings a large volume of data. Classical coding methods are not effective to describe the relationship between different views, leading to redundancy left. To address this problem, we propose a novel light field compression scheme based on implicit neural representation to reduce redundancies between views. We store the information of a light field image implicitly in an neural network and adopt model compression methods to further compress the implicit representation. Extensive experiments have demonstrated the effectiveness of our proposed method, which achieves comparable rate-distortion performance as well as superior perceptual quality over traditional methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Redundancy

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A Conflict-Priority-Based Variable Neighborhood Tabu Search Method for Multi-Satellite Scheduling

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Advances in Astronautics Science and technology 2024年第2期7卷 163-176页

作者： Chen, Xiaoyu Gao, Qi Peng, Shengren Song, Shihui Liu, Yufei Dai, Guangming Wang, Maocai Zhang, Changxing School of Computer China University of Geosciences Lumo Road Wuhan430074 China Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Lumo Road Wuhan430074 China Qian Xuesen Laboratory China Academy of Space Technology Youyi Road Beijing100094 China

With the increasing number and capabilities of orbital satellites and their onboard sensors, satellites scheduling plays an important role in growing demands for Earth observation tasks. A conflict-priority-based variable neighborhood tabu search method is proposed in this paper. By analyzing the flexibility of resources, the conflict-priority of tasks, and the visible time window features, the indicators of the impact, the conflict, the flexibility, and the bad impact are first established. On this basis, a first-come first-served greedy algorithm and a minimum bad impact greedy algorithm are designed for the generation of the initial solution. some rule-based heuristic strategies are also adopted for optimizing. Additionally, the algorithm incorporates three types of neighborhood structures including insertion, swapping and rearrangement, and deletion, using a tabu list to avoid local optima. Experimental results indicate the validity and efficiency of the proposed method. All solutions are within 10% of the optimal solution, with some even within 5%.The proposed method is effective for obtaining optimum solutions or solutions with a very good quality in various multi-satellite scheduling scenarios of realistic planning. © Chinese Society of Astronautics 2024.

关键词： Tabu search

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spatial Decomposition and Temporal Fusion based Inter Prediction for Learned Video Compression

arXiv

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arXiv 2024年

作者： Sheng, Xihua Li, Li Liu, Dong Li, Houqiang The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

Video compression performance is closely related to the accuracy of inter prediction. It tends to be difficult to obtain accurate inter prediction for the local video regions with inconsistent motion and occlusion. Traditional video coding standards propose various technologies to handle motion inconsistency and occlusion, such as recursive partitions, geometric partitions, and long-term references. However, existing learned video compression schemes focus on obtaining an overall minimized prediction error averaged over all regions while ignoring the motion inconsistency and occlusion in local regions. In this paper, we propose a spatial decomposition and temporal fusion based inter prediction for learned video compression. To handle motion inconsistency, we propose to decompose the video into structure and detail (SDD) components first. Then we perform SDD-based motion estimation and SDD-based temporal context mining for the structure and detail components to generate short-term temporal contexts. To handle occlusion, we propose to propagate long-term temporal contexts by recurrently accumulating the temporal information of each historical reference feature and fuse them with short-term temporal contexts. With the SDD-based motion model and long short-term temporal contexts fusion, our proposed learned video codec can obtain more accurate inter prediction. Comprehensive experimental results demonstrate that our codec outperforms the reference software of H.266/VVC on all common test datasets for both PSNR and MS-SSIM. Copyright © 2024, The Authors. All rights reserved.

关键词： Image compression

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Integrating Regularization and PnP Priors for SAR Image Reconstruction Using Multi-agent Consensus Equilibrium

Integrating Regularization and PnP Priors for SAR Image Reco...

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2024 International Workshop on the Theory of Computational Sensing and its Applications to Radar, Multimodal Sensing and Imaging, CoSeRa 2024

作者： Fan, Yizhe Liu, Mingqian Shao, Mingxiao Zhang, Bingchen Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System China University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering China Aeronautical Remote Sensing Center Chinese Academy of Sciences China

ISBN: (纸本)9798350365504

Multi-agent consensus equilibrium mechanism is a generalization of popular used PnP-ADMM method and composite regularization in computational sensing. We propose a novel SAR image sparse reconstruction method based on MACE mechanism. High-quality SAR images necessitate low ambiguity, high SNR, and low coherent speckle. We therefore integrate regularization priors and PnP priors for azimuth ambiguity suppression, sparsity inducing and despeckling. The proposed method's performance is evaluated through experiments on both simulated and QILU-1 satellite SAR data. © 2024 IEEE.

关键词： Image reconstruction

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Wavelet-Like Transform-Based technology in Response to the Call for Proposals on Neural Network-Based Image Coding

arXiv

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arXiv 2024年

作者： Dong, Cunhui Ma, Haichuan Zhang, Haotian Gao, Changsheng Li, Li Liu, Dong The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

Neural network-based image coding has been developing rapidly since its birth. Until 2022, its performance has surpassed that of the best-performing traditional image coding framework – H.266/VVC. Witnessing such success, the IEEE 1857.11 working subgroup initializes a neural network-based image coding standard project and issues a corresponding call for proposals (CfP). In response to the CfP, this paper introduces a novel wavelet-like transform-based end-to-end image coding framework – iWaveV3. iWaveV3 incorporates many new features such as affine wavelet-like transform, perceptual-friendly quality metric, and more advanced training and online optimization strategies into our previous wavelet-like transform-based framework iWave++. While preserving the features of supporting lossy and lossless compression simultaneously, iWaveV3 also achieves state-of-the-art compression efficiency for objective quality and is very competitive for perceptual quality. As a result, iWaveV3 is adopted as a candidate scheme for developing the IEEE Standard for neural-network-based image coding. Copyright © 2024, The Authors. All rights reserved.

关键词： Image coding

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Modification of the RUSLE slope length factor based on a multiple flow algorithm considering vertical leakage at karst landscapes

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International Soil and Water Conservation Research 2024年第2期12卷 446-454页

作者： Teng Feng Yuemin Yue Kelin Wang Hongsong Chen Lu Zhai Xianzhao Liu Yuanqi Chen Yong Zhang School of Earth Sciences and Spatial Information Engineering Hunan University of Science and TechnologyXiangtan411201HunanChina Guangxi Key Laboratory of Karst Ecological Processes and Services Institute of Subtropical AgricultureChinese Academy of SciencesChangsha410125HunanChina Huanjiang Observation and Research Station for Karst Ecosystems Chinese Academy of SciencesHuanjiang547100GuangxiChina Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying Mapping and Remote SensingHunan University of Science and TechnologyXiangtan411201HunanChina

Heterogeneous karst surfaces exerted scaling effects whereby specific runoff decrease with increasing *** existing RUSLE-L equations are limited by the default implicit assumption that the surface-runoff intensity is constant at any slope *** objective of this study was to modify the L-equation by establishing the functional relationship between surface-runoff intensity and karst slope length,and to evaluate its predictive capability at different resolution *** grid layers were generated based on the area rate of surface karstification and considered the runoff transmission percentage at the exposed karst fractures or conduits to be *** the multiple flow direction algorithm united with the transfer grid(MFDTG),the flow accumulation of each grid cell was simulated to estimate the average surface-runoff intensity over different slope *** effectiveness of MFDTG algorithm was validated by runoff plot data in Southwestern *** simulated results in a typical peak-cluster depression basin with an area rate of surface karstification of 6.5%showed that the relationship between surface-runoff intensity and slope length was a negative power *** by the proposed modified L-equation((al_(x)^((b+1))/22.13)^(m)),the L-factor averages of the study basin ranged from 0.35 to 0.41 at 1,5,25 and 90 m resolutions *** study indicated that the modified L-equation enables an improved prediction of the much smaller L-factor and the use of any resolution DEMs on karst *** attention should be given to the variation of surface-runoff intensity with slope length when predicting L-factor on hillslopes with runoff scale effect.

关键词： RUSLE-L factor Modified equation Scale effect Runoff transmission loss Flow algorithm Transfer grid

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Analysis of the Fluence of Multi-sources DEM in Remote Sensing Image Ortho-rectification

Analysis of the Fluence of Multi-sources DEM in Remote Sensi...

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2024 International Conference on Remote Sensing, Mapping, and Image processing, RSMIP 2024

作者： Liu, Rui Zhu, Jingxing Jiao, Niangang Wang, Feng You, Hongjian Xiang, Yuming Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 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)9781510680012

Digital Elevation Models (DEMs) depict the configuration of the Earth surface, which is essential for remote sensing image ortho-rectification. Nowadays, the ALOS DEM, ASTER GDEM and SRTM DEM are the most commonly used models, produced based on different remote sensing datasets. geolocation errors may be introduced with different producing methods and datasets. Therefore, multi-sources remote sensing images covering various terrain in China are experimented for the analysis of ortho-rectification accuracy using different DEMs. Based on the rational function model, the geolocation error of original imagery can be reduced greatly with the grounds control points, and different DEMs are used for the ortho-rectification of different imagery. The accuracy is experimented and verified using high-precision reference images. Experimental results show that ortho-rectificated images with the ALOS DEM can achieve better accuracy compared with the SRTM DEM and ASTER GDEM, which is significant for geometric processing of remote sensing images. © 2024 SPIE.

关键词： Remote sensing

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SAR Tomographic Inversion of Urban Areas via Morphology Regularization 15

SAR Tomographic Inversion of Urban Areas via Morphology Regu...

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15th European Conference on Synthetic Aperture Radar, EUSAR 2024

作者： Li, Jie Li, Zhiyuan Fan, Yizhe 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 Beijing101408 China

ISBN: (纸本)9783800762873

The current synthetic aperture radar (SAR) images with ultra high resolution provide the detailed structures of the urban areas, which are often utilized to retrieve 3D spatial information of the detailed structures based on tomographic synthetic aperture radar (TomoSAR). Compressive sensing (CS), as a favorable sparse reconstruction technique has been widely used in TomoSAR, however, the sparsity constraints by CS algorithms often lose detailed structures of the targets due to the pixel-wise processing. In this paper, we apply the morphology regularization as a prior term to form a novel approach based on the CS algorithm. The morphology regularization can shrink the concatenations caused by outliers in the iterative reconstruction, enhancing the detailed structural properties of reconstructed targets. And the alternating direction method of multipliers (ADMM) is applied, where the Bregman iteration is adopted for solving the sub-problem with the morphology regularization. Both simulation experiments and tests on real data show that the proposed method can suppress the outliers and preserve the detailed structures of tomographic reconstruction, leading to improved correctness and completeness. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Morphology

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SAR Image Compression Based on Low-Frequency Suppression and Target Perception 8th

SAR Image Compression Based on Low-Frequency Suppression and...

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8th EAI International Conference on Machine Learning and Intelligent Communications, MLICOM 2023

作者： Deng, Jiawen Huang, Lijia Wu, Yifan Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783031717154

Synthetic Aperture Radar (SAR) images are widely used in the field of remote sensing. To store and transmit the growing amount of SAR image data, more efficient compression algorithms are required. In this paper, a new framework for compressing SAR images is proposed based on deep learning. Firstly, we propose a new two-stage transformation based on low-frequency suppression of input data to achieve high information entropy and low quantization loss. To explore the redundancy between regions of interest and regions of non-interest, an image compression model for target perception is proposed to convert the input SAR image into a compact latent representation together with the target perception map. To evaluate the performance of the algorithm, we conducted experiments on SAR image dataset. The results show that the proposed algorithm is significantly better than the traditional processing algorithm in terms of data input, and can effectively preserve and improve the performance of the image compression model. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Image compression

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SAR Change Detection Based on keypoint Graph Structure and Cosegmentation-Based Refinement

SAR Change Detection Based on Keypoint Graph Structure and C...

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geology, Mapping and Remote Sensing (ICGMRS), International Conference on

作者： Jingxing Zhu Rui Liu Niangang Jiao Feng Wang Hongjian You Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Aerospace information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350365719

ISBN: (纸本)9798350365726

Change detection between high-resolution Synthetic Aperture Radar (SAR) images remains a challenging task. Due to the effectively integration of structure and intensity information, graph-based methods have made some progress in SAR change detection. But it still faces the issues of robustness of graph construction and incomplete change detection outcomes. To tackle these challenges, we propose an improved keypoint-based graph structure change detection approach. First, we propose a keypoint extraction strategy guided by a coarse change map to better describe potential change areas. Second, we construct stable graph structures on these keypoints with employing a robust distance measurement that considers the statistical properties of SAR images. Then, the keypoint difference image (KDI) is derived through measuring the consistency of graph structures between pre-and post-time SAR images. Next, keypoint change map (KCM) is generated through clustering KDI by k-means. Finally, we use co-segmentation superpixel mask to refine the KCM and obtain accurate and complete binary change map. We validate the effective of proposed framework on GF-3 dataset. Experimental results demonstrate the superior performance of our method compared to alternative method.

关键词： Image segmentation geology Noise Focusing Speckle Radar polarimetry Robustness

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