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IEEE International Conference on Radar

作者： Junying Yang Xiaolan Qiu Mingyang Shang Lihua Zhong Yini Lv Chibiao Ding Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing China National Key Laboratory of Microwave Imaging Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665468893

For moving targets in synthetic aperture radar (SAR) images, the obvious features are defocusing and dislocation. To estimate motion parameters accurately is a premise for the precise imaging of moving targets. However, when the radial velocity of the target exceeds the maximum detectable unambiguous velocity, the estimated value by the existing methods is no longer the real value. A radial velocity ambiguity resolution method based on Imaging Space-Time Adaptive processing (ISTAP) is proposed in this paper. The proposed method has no search or iterative process and is suitable for the azimuth multichannel SAR with low pulse repetition frequency (PRF). Finally, the simulated data from an x-band azimuth six-channel SAR system verify the feasibility of the proposed method.

关键词： Baseband Image resolution Azimuth Energy resolution Imaging Estimation Radar imaging

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Efficient Sparse MIMO SAR Imaging with Fast Iterative Method Based on Back Projection and Approximated Observation

Efficient Sparse MIMO SAR Imaging with Fast Iterative Method...

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Electronics and Electrical Engineering technology (EEET), International Conference on

作者： Pengyu Jiang Zhe Zhang Bingchen Zhang Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Suzhou Aerospace Information Research Institute Suzhou China

Compressed sensing technique is widely used in the field of multiple input multiple output (MIMO) radar imaging to suppress sidelobes and noise, bringing better imaging performance. However, many constraints are introduced to the sparse reconstruction model, current sparse microwave imaging model is based on several assumptions, such as far field assumption, which might not hold in some circumstances. In this paper, a novel two-dimensional sparse reconstruction method based on approximated observation is proposed. First, we obtain the measurement matrix by the inverse back-projection operator, and construct a two-dimensional sparse reconstruction model. Then, we adopt the minimax concave constraint as the regularization term and use the iterative soft threshold algorithm (ISTA) for sparse reconstruction. The method retains the advantages of the BP algorithm and the sparse reconstruction algorithm at the same time, and the distance between the target and the antenna array is not constrained by image reconstruction performance of the sparse reconstruction algorithm. Simulations and experiments demonstrate the effectiveness of the proposed method.

关键词： MIMO radar Simulation Imaging Radar imaging Reconstruction algorithms Sparse matrices Velocity measurement

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Temporal Context Mining for Learned Video Compression

arXiv

引用

arXiv 2021年

作者： Sheng, Xihua Li, Jiahao Li, Bin Li, Li Liu, Dong Lu, Yan The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China Microsoft Research Asia Beijing100080 China

Applying deep learning to video compression has attracted increasing attention in recent few years. In this work, we address end-to-end learned video compression with a special focus on better learning and utilizing temporal contexts. We propose to propagate not only the last reconstructed frame but also the feature before obtaining the reconstructed frame for temporal context mining. From the propagated feature, we learn multi-scale temporal contexts and re-fill the learned temporal contexts into the modules of our compression scheme, including the contextual encoder-decoder, the frame generator, and the temporal context encoder. We discard the parallelizationunfriendly auto-regressive entropy model to pursue a more practical encoding and decoding time. Experimental results show that our proposed scheme achieves a higher compression ratio than the existing learned video codecs. Our scheme also outperforms x264 and x265 (representing industrial software for H.264 and H.265, respectively) as well as the official reference software for H.264, H.265, and H.266 (JM, HM, and VTM, respectively). Specifically, when intra period is 32 and oriented to PSNR, our scheme outperforms H.265-HM by 14.4% bit rate saving;when oriented to MS-SSIM, our scheme outperforms H.266-VTM by 21.1% bit rate saving. Copyright © 2021, The Authors. All rights reserved.

关键词： Image compression

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No-reference quality assessment for 360-degree images by analysis of multi-frequency information and local-global naturalness

arXiv

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arXiv 2021年

作者： Zhou, Wei Xu, Jiahua Jiang, Qiuping Chen, Zhibo CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China School of Information Science and Engineering Ningbo University Ningbo315211 China

360-degree/omnidirectional images (OIs) have achieved remarkable attentions due to the increasing applications of virtual reality (VR). Compared to conventional 2D images, OIs can provide more immersive experience to consumers, benefitting from the higher resolution and plentiful field of views (FoVs). Moreover, observing OIs is usually in the head mounted display (HMD) without references. Therefore, an efficient blind quality assessment method, which is specifically designed for 360-degree images, is urgently desired. In this paper, motivated by the characteristics of the human visual system (HVS) and the viewing process of VR visual contents, we propose a novel and effective no-reference omnidirectional image quality assessment (NR OIQA) algorithm by Multi-Frequency information and Local-Global Naturalness (MFILGN). Specifically, inspired by the frequency-dependent property of visual cortex, we first decompose the projected equirectangular projection (ERP) maps into wavelet subbands by using discrete Haar wavelet transform (DHWT). Then, the entropy intensities of low-frequency and high-frequency subbands are exploited to measure the multi-frequency information of OIs. Besides, except for considering the global naturalness of ERP maps, owing to the browsed FoVs, we extract the natural scene statistics (NSS) features from each viewport image as the measure of local naturalness. With the proposed multi-frequency information measurement and local-global naturalness measurement, we utilize support vector regression (SVR) as the final image quality regressor to train the quality evaluation model from visual quality-related features to human ratings. To our knowledge, the proposed model is the first no-reference quality assessment method for 360-degreee images that combines multi-frequency information and image naturalness. Experimental results on two publicly available OIQA databases demonstrate that our proposed MFILGN outperforms state-of-the-art full-reference (FR) a

关键词： Helmet mounted displays

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A SAR imaging method based on generalized minimax-concave penalty

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Science China(information Sciences) 2019年第2期62卷 233-239页

作者： Zhonghao WEI Bingchen ZHANG Yirong WU University of Chinese Academy of Sciences Key Laboratory of Technology in Geospatial Information Processing and Application Systems Institute of Electronics Chinese Academy of Sciences Institute of Electronics Chinese Academy of Sciences

Dear editor,Sparse signal processing offers a framework for synthetic aperture radar （SAR） imaging [1, 2]. As an efficient tool in sparse signal processing, L1minimization is often used in the reconstruction of SAR images. When implemented in SAR imaging [3–5], L1minimization offers significant improvement in the properties by suppressing the sidelobes and clutter. However, L1minimization is known to be a biased estimator. The L1minimization based algorithms such as the iterative

关键词： SAR A SAR imaging method based on generalized minimax-concave penalty

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A Multiple Moving Ships Detection Method for GF-4 Satellite Image in Thin-Cloud Environment 1

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6th China High Resolution Earth Observation Conference, CHREOC 2019

作者： Lv, Peng Hu, Yuxin Li, Qianqian Hou, Yangshuan Wang, Xiaohui Lei, Bin Institute of Electronics Chinese Academy of Sciences Beijing China Key Laboratory of Spatial Information Processing and Application System Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9789811539473

ISBN: (纸本)9789811539466

At present, the GF-4 satellite is the world’s highest resolution geostationary orbit optical imaging satellite. The GF-4 satellite has the advantages of wide-swath and high-frequency imaging, so it can provide quasi-real-time images for ship surveillance. In fact, using the GF-4 satellite image to detect moving ships is to detect the gray value of the wake formed by the ship moving at sea. However, because of the geometric features of the ship’s wake vary with the speed, the ship detection method based on geometric features is not applicable. Since, not only the ship is moving in the image but also the cloud is moving all the time, the optical flow method is not applicable. Ship detection methods based on machine learning and deep learning are also not applicable due to too few texture features of the wake. In this paper, we present a novel method for ship detection in thin-cloud environments. The real image processing of GF-4 satellite shows that the proposed method can detect multiple moving ships accurately and quickly in a thin-cloud environment. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Ships

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Preliminary Analysis of PolInSAR Scattering Mechanism in Urban Areas through UAV-borne PolInSAR system

Preliminary Analysis of PolInSAR Scattering Mechanism in Urb...

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IEEE International Conference on Radar

作者： Zexin Lv Xiaolan Qiu Chibiao Ding Key Laboratory of Technology in Geo-spatial Information Processing and Application System Beijing China Aerospace Information Research Institute (of Chinese Academy of Sciences) Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Aerospace Big Dara Application Technology Suzhou China Suzhou Aerospace Information Research Institute Suzhou China

ISBN: (纸本)9781665468893

Polarimetric Interferometric SAR (PolInSAR) can improve the coherence of images and it plays an important role in urban remote sensing. The explanation of its scattering mechanism is concerned by many researchers. It is always explained from the aspect of different polarization decompositions but seldom from the height of scattering centers. In this paper, a Ku-band UAV-borne PolInSAR system is used to compare the relationship of scattering centers between PolInSAR and Pauli decomposition on different artificial targets. The physical meaning of PolInSAR is preliminary analyzed and our conclusion is also verified through a C-band manned airborne PolInSAR system.

关键词： C-band Airborne radar Urban areas Scattering Coherence Radar polarimetry Synthetic aperture radar

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A Close Look at Few-shot Real Image Super-resolution from the Distortion Relation Perspective

arXiv

引用

arXiv 2021年

作者： Li, Xin Jin, Xin Fu, Jun Yu, Xiaoyuan Tong, Bei Chen, Zhibo The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China The Eastern Institute for Advanced Study China Huawei Cloud China

Collecting amounts of distorted/clean image pairs in the real world is non-trivial, which seriously limits the practical applications of these supervised learning-based methods on real-world image super-resolution (RealSR). Previous works usually address this problem by leveraging unsupervised learning-based technologies to alleviate the dependency on paired training samples. However, these methods typically suffer from unsatisfactory texture synthesis due to the lack of supervision of clean images. To overcome this problem, we are the first to have a close look at the under-explored direction for RealSR, i.e., few-shot real-world image super-resolution, which aims to tackle the challenging RealSR problem with few-shot distorted/clean image pairs. Under this brand-new scenario, we propose Distortion Relation guided Transfer Learning (DRTL) for the few-shot RealSR by transferring the rich restoration knowledge from auxiliary distortions (i.e., synthetic distortions) to the target RealSR under the guidance of distortion relation. Concretely, DRTL builds a knowledge graph to capture the distortion relation between auxiliary distortions and target distortion (i.e., real distortions in RealSR). Based on the distortion relation, DRTL adopts a gradient reweighting strategy to guide the knowledge transfer process between auxiliary distortions and target distortions. In this way, DRTL could quickly learn the most relevant knowledge from the synthetic distortions for the target distortion. We instantiate DRTL with two commonly-used transfer learning paradigms, including pre-training and meta-learning pipelines, to realize a distortion relation-aware Few-shot RealSR. Extensive experiments on multiple benchmarks and thorough ablation studies demonstrate the effectiveness of our DRTL. Copyright © 2021, The Authors. All rights reserved.

关键词： Textures

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Intertidal area classification with generalized extreme value distribution and Markov random field in quad-polarimetric synthetic aperture radar imagery

引用

Frontiers of information technology & Electronic Engineering 2019年第2期20卷 253-264页

作者： Ting-ting JIN Xiao-qiang SHE Xiao-lan QIU Bin LEI Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of ElectronicsChinese Academy of Sciences University of Chinese Academy of Sciences Huawei Software Technologies Co. Ltd.

Classification of intertidal area in synthetic aperture radar(SAR) images is an important yet challenging issue when considering the complicatedly and dramatically changing features of tidal fluctuation. The difficulty of intertidal area classification is compounded because a high proportion of this area is frequently flooded by water, making statistical modeling methods with spatial contextual information often ineffective. Because polarimetric entropy and anisotropy play significant roles in characterizing intertidal areas, in this paper we propose a novel unsupervised contextual classification algorithm. The key point of the method is to combine the generalized extreme value(GEV) statistical model of the polarization features and the Markov random field(MRF) for contextual smoothing. A goodness-of-fit test is added to determine the significance of the components of the statistical model. The final classification results are obtained by effectively combining the results of polarimetric entropy and anisotropy. Experimental results of the polarimetric data obtained by the Chinese Gaofen-3 SAR satellite demonstrate the feasibility and superiority of the proposed classification algorithm.

关键词： Intertidal classification Polarimetric synthetic aperture radar Finite mixture model Markov random field Generalized extreme value model

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Spectral Characteristics and Model Inversion of Common Greening Plants in Guangzhou Under Dust Pollution 7th

Spectral Characteristics and Model Inversion of Common Green...

引用

7th International Conference on Geoinformatics in Sustainable Ecosystem and Society, GSES 2019, and 1st International Conference on geospatial Artificial Intelligence for Urban Computing, GeoAI 2019

作者： Zhang, Chen Zhou, Xia Li, Yong Yang, Ji Yang, Chuanxun Wen, Kaixiang Chen, Yuchan Yang, Libo Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou Institute of Geography Guangzhou510070 China Guangzhou511458 China State Grid Hunan Maintenance Company Hengyang421000 China

ISBN: (纸本)9789811561054

Hyperspectral remote sensing provides a highly efficient, convenient, and non-destructive technical means for the quantitative study of the dust retention content (DRC) of plants. Based on the common greening plants in Guangzhou, this study used hyperspectral technology to explore the spectral characteristics of different plant leaves under dust pollution, and used the Pearson correlation method to construct the DRC estimation models of plants. The results show that the spectral characteristics of the dust-retention effect of plants are mainly concentrated in the visible and near-infrared wavelengths (350–1360 nm), which are important band intervals for the study of spectral characteristics and the inversion band selection. The first derivative of spectral reflectance is more suitable for constructing the DRC estimation model than the spectral reflectance, and CF has a good fitting effect, and the deviation between the Model estimated DRC and the measured DRC is small, and its R2 is 0.55 and the RMSE is 0.300 g/m2. Through this research, the application of hyperspectral remote sensing technology in the dust retention effect research of plants can be effectively promoted, which can help to quickly and non-destructively evaluate the DRC of plants, and provide a scientific basis for rational selection of green plant types and urban air quality monitoring, and lay a foundation for large-scale DRC estimation. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Reflection

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