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IEEE Transactions on Artificial Intelligence 2021年第1期2卷 58-70页

作者： Li, Ziqiang Tao, Rentuo Wang, Jie Li, Fu Niu, Hongjing Yue, Mingdao Li, Bin The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems University of Science and Technology of China Hefei230052 China The Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230052 China The School of Information Science and Technology University of Science and Technology of China Hefei230052 China The College of Mechanical and Electrical Engineering Suzhou University Suzhou234000 China

With the success of generative adversarial networks (GANs) on various real-world applications, the controllability and security of GANs have raised more and more concerns from the community. Specifically, understanding the latent space of GANs, i.e., obtaining the completely decoupled latent space, is essential for applications in some secure scenarios. At present, there is no quantitative method to measure the decoupling of latent space, which is not conducive to the development of the community. In this article, we propose two methods to measure the sensitivity of latent dimensions: one is a sequential intervention method, and the other is an optimization-based method that measures the sensitivity in both the value and the direction. With these two methods, the decoupling of latent space can be measured by the sparsity of the sensitivity vector obtained. The effectiveness of the proposed methods has been verified by experiments on the representative GANs. Code will be available at https://***/iceli1007/latent-analysis-of. Impact Statement-Generative adversarial networks (GANs) is a popular technology in image generation. Benefiting from the development of neural networks, GANs have been widely used in many real-world tasks, such as super-resolution, image translation, and image inpainting. However, the understanding of the generator in GANs as a map from latent space (represented in a multidimensional vector) to image space is incomplete. To make better use of GANs, more insight into the generation process is required. The motivation of this article is to develop methods to analyze the influence of each dimension of latent space on the generated results, and to measure the decoupling of latent space, the knowledge obtained will be useful for the development of more precise controllable image generation technology. © 2021 IEEE.

关键词： Generative adversarial networks

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A novel error concealment algorithm for H.264/AVC 21

A novel error concealment algorithm for H.264/AVC

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21st International Conference on MultiMedia Modeling, MMM 2015

作者： Zhang, Jinlei Li, Houqiang Chinese Academy of Sciences Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

ISBN: (纸本)9783319144443

To benefit network transmission, the bit stream of the whole frame coded by H.264/AVC is usually grouped into one packet. However, the packet loss during transmission will lead to the distortion of the reconstructed video and the error propagation. To deal with this problem, error concealment (EC) strategy is widely used to recover the lost frames and to weaken the effect of error propagation. In this paper, we propose a new EC method which uses both the forward and backward motion information to recover the motion information of the current lost frame. Besides, for the pixels whose motion information is quite different compared with its neighboring pixels, we propose to use the spatial correlation to fill up the pixels by minimizing the total variation (TV) *** the help of our proposed algorithm, we can obtain the motion vectors of the lost frames and improve the accuracy of motion vectors derived by the unidirection recovery method. Besides, the optimization strategy, minimizing the TV-norm for the pixels with quite different motion vectors, can help the decoder to recover the reconstructed frames more accurately. Experimental results show that the proposed algorithm can achieve both better objective performance and subjective performance compared to well-known schemes. © Springer International Publishing Switzerland 2015.

关键词： Recovery

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Non-negative matrix factorization based unmixing for principal component transformed hyperspectral data

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Frontiers of information technology & Electronic Engineering 2016年第5期17卷 403-412页

作者： Xiu-rui GENG Lu-yan JI Kang SUN Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing 100190 China MOE Key Laboratory for Earth System Modeling Center for Earth System Science Tsinghua University Beijing 100084 China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang 050000 China

Non-negative matrix factorization(NMF) has been widely used in mixture analysis for hyperspectral remote sensing. When used for spectral unmixing analysis, however, it has two main shortcomings:(1) since the dimensionality of hyperspectral data is usually very large, NMF tends to suffer from large computational complexity for the popular multiplicative iteration rule;(2) NMF is sensitive to noise(outliers), and thus the corrupted data will make the results of NMF meaningless. Although principal component analysis(PCA) can be used to mitigate these two problems, the transformed data will contain negative numbers, hindering the direct use of the multiplicative iteration rule of NMF. In this paper, we analyze the impact of PCA on NMF, and find that multiplicative NMF can also be applicable to data after principal component transformation. Based on this conclusion, we present a method to perform NMF in the principal component space, named ‘principal component NMF'(PCNMF). Experimental results show that PCNMF is both accurate and time-saving.

关键词： Non-negative matrix factorization(NMF) Principal component analysis(PCA) Endmember Hyperspectral

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TuiGAN: Learning Versatile Image-to-Image Translation with Two Unpaired Images 1

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16th European Conference on Computer Vision, ECCV 2020

作者： Lin, Jianxin Pang, Yingxue Xia, Yingce Chen, Zhibo Luo, Jiebo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China Microsoft Research Asia Beijing China University of Rochester Rochester United States

ISBN: (数字)9783030585488

ISBN: (纸本)9783030585471

An unsupervised image-to-image translation (UI2I) task deals with learning a mapping between two domains without paired images. While existing UI2I methods usually require numerous unpaired images from different domains for training, there are many scenarios where training data is quite limited. In this paper, we argue that even if each domain contains a single image, UI2I can still be achieved. To this end, we propose TuiGAN, a generative model that is trained on only two unpaired images and amounts to one-shot unsupervised learning. With TuiGAN, an image is translated in a coarse-to-fine manner where the generated image is gradually refined from global structures to local details. We conduct extensive experiments to verify that our versatile method can outperform strong baselines on a wide variety of UI2I tasks. Moreover, TuiGAN is capable of achieving comparable performance with the state-of-the-art UI2I models trained with sufficient data. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Retrieval of Significant Wave Height Under Typhoon Conditions from Gaofen-3 SAR Imagery

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Journal of Ocean University of China 2022年第1期21卷 81-90页

作者： WANG Xiaochen HAN Bing ZHONG Lihua YUAN Xinzhe Aerospace Information Research Institute Chinese Academy of ScienceBeijing 100190China Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Aerospace Information Research InstituteChinese Academy of ScienceBeijing 100190China The National Satellite Ocean Applications Center Beijing 100081China

The objective of this paper is to propose an empirical method to inverse significant wave height(SWH)under typhoon conditions from collected dual-polarization Gaofen(GF)-3 synthetic aperture radar(SAR)*** typhoon scenes were cap-tured from narrow scan(NSC)and wide scan(WSC)images,and collocated with European Center for Medium-Range Weather Fore-casts reanalysis data of(ECMWF).To improve the quality of GF-3 SAR images,the recalibration over rainforest and de-scalloping were carried *** establish the empirical relationship between SAR-derived parameters and collocated SWH,the sensitivity analysis of typical parameters about the normalized radar cross section(Nrcs)and imagery variance(Cvar)were performed to both VV and VH polarized *** scenes from GF-3 SAR imagery under typhoon conditions were used for training the model by the multivari-ate least square regression,and one scene was used for preliminary *** was found that the joint retrieval model based on VV and VH polarized SAR imagery performed better than any single polarized *** results,verified by using ECMWF data,revealed the soundness of this approach,with a correlation of 0.95,bias of 0 m,RMSE of 0.44 and SI of 0.01 when VV polarization and VH polarization data were both used.

关键词： Gaofen-3 SAR typhoon significant wave height

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WATERMARKING WITH REFINED PERCEPTUAL MASKING TUNED FOR SYNTHETIC APERTURE RADAR IMAGES

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Journal of Electronics(China) 2014年第4期31卷 310-316页

作者： Lei Bin Ding Chibiao Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Institute of Electronics Chinese Academy of Sciences University of Chinese Academy of Sciences

A watermarking scheme designed for remote sensing images needs to meet the same demand of both invisibility as for ordinary digital images. Due to specific perceptual characteristics of Synthetic Aperture Radar(SAR) images, the watermarking algorithms with consideration of Human Vision System(HVS) modeling from optical images give poor performance when applied on SAR images. This paper examines a variety of factors affecting the noise sensitivity, and further proposes a refined pixel-wise masking approach for watermarking on SAR images. The proposed approach is applied on logarithmic transformed SAR images, and has increased the acceptable watermark embedding strength by about 6 dB to 10 dB while achieving the same levels of watermarked image visual quality. Experimental results show that this approach enhanced the perceptual invisibility of watermarking based on wavelet decomposition.

关键词： Digital watermarking Synthetic Aperture Radar （SAR） Perceptual masking WaveletCLC number：TN957

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Visual analyses of music download history: User studies 22nd

Visual analyses of music download history: User studies

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22nd International Conference on MultiMedia Modeling, MMM 2016

作者： Liu, Dong Zhang, Jingxian CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China Department of Computer Science University of Illinois at Urbana-Champaign ChampaignIL United States

ISBN: (纸本)9783319276700

Users’ download history is a primary data source for analyzing user interests. Recent work has shown that user interests are indeed time varying, and accurate profiling of user interest drifts requires the temporal dynamic analyses. We have proposed a visualization approach to analyzing user interest drifts from the download history, taking music as an example, and studied how to depict the underlying relevances among the downloaded music items to identify the drifts. We designed three new kinds of plots to display the music download history of one user, namely Bean plot, Transitional Pie plot, and Instrument plot. In this paper, we report our conducted user studies that ask normal users to visually analyze the download history of other users in a given realworld data set. User studies are performed in a learning-practice-test workflow. The results demonstrate the feasibility of our visualization design. © Springer International Publishing Switzerland 2016.

关键词： Visualization

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An accurate SAR imaging method based on generalized minimax concave penalty 12

An accurate SAR imaging method based on generalized minimax ...

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12th European Conference on Synthetic Aperture Radar, EUSAR 2018

作者： Wei, Zhonghao Zhang, Bingchen Han, Bing Xu, Zhilin Hong, Wen Wu, YiRong University of Chinese Academy of Science and Key Laboratory of Technology in Geo-spatial Information Processing and Application Systems Institute of Electronics Chinese Academy of Sciences China Laboratory of Technology in Geo-spatial Information Processing and Application Systems Institute of Electronics Chinese Academy of Sciences China Institute of Electronics Chinese Academy of Sciences China

ISBN: (纸本)9783800746361

Sparse signal processing has been applied in synthetic aperture radar (SAR) imaging successfully. As a typical sparse reconstruction model, L1 regularization often underestimates the intensities of the targets. The estimated radar cross section (RCS) is related to the pixel intensity. The underestimation will cause the errors in RCS estimations. In this paper, we present a SAR imaging method based on generalized minimax convex (GMC) penalty, which can avoid the pixel intensity underestimation. The performance of the proposed method is verified using simulations and real data. © VDE VERLAG GMBH Â Berlin Â Offenbach.

关键词： Radar cross section

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HPHR-SAR-Net: Hyperpixel High-Resolution SAR Imaging Network Based on Nonlocal Total Variation

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2023年 16卷 8595-8608页

作者： Zhou, Guoru Xu, Zhongqiu Fan, Yizhe Zhang, Zhe Qiu, Xiaolan Zhang, Bingchen Fu, Kun Wu, Yirong Chinese Academy of Sciences Key Laboratory of Technology in Geo-spatial Information Processing and Application System Beijing100045 China University of Chinese Academy of Sciences Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100045 China Suzhou Aerospace Information Research Institute Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou215009 China

High resolution is a key trend in the development of synthetic aperture radar (SAR), which enables the capture of fine details and accurate representation of backscattering properties. However, traditional high-resolution SAR imaging algorithms face several challenges. First, these algorithms tend to focus on local information, neglecting nonlocal information between different pixel patches. Second, speckle is more pronounced and difficult to filter out in high-resolution SAR images. Third, the process of high-resolution SAR imaging generally involves high time and computational complexity, making real-time imaging difficult to achieve. To address these issues, we propose a hyperpixel high-resolution SAR imaging network (HPHR-SAR-Net) for rapid despeckling in high-resolution modes. Based on the concept of hyperpixel techniques, we initially combine nonconvex and nonlocal total variation as compound regularization. This approach more effectively despeckles and manages the relationship between pixels while reducing bias effects caused by convex constraints. Subsequently, we solve the compound regularization model using the alternating direction method of multipliers (ADMM) algorithm and unfold it into a deep unfolding network (DUN). The network's parameters are adaptively learned in a data-driven manner, and the learned network significantly increases imaging speed. Additionally, the proposed HPHR-SAR-Net is compatible with high-resolution imaging modes, such as spotlight, staring spotlight, and sliding spotlight. In this article, we demonstrate the superiority of HPHR-SAR-Net through experiments in both simulated and real SAR scenarios. The results indicate that HPHR-SAR-Net can rapidly perform high-resolution SAR imaging from raw echo data, producing accurate imaging results. © 2008-2012 IEEE.

关键词： Radar imaging

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Stabilization of Time-Delay Markovian Jump Systems via Probability Rate Synthesis and State Feedback

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IFAC Proceedings Volumes 2014年第3期47卷 10415-10420页

作者： Shan Ma Junlin Xiong CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei 230026 China

This paper considers the stabilization problem for Markovian jump systems with time delays. Both the probability rate matrix and the state feedback control law are to be designed. A sufficient condition is established for such designs such that the resulting closed-loop Markovian jump system is stochastically stable. This condition is given in terms of a system of linear matrix inequalities with rank constraints, and can be solved using some existing algorithms. When the system has polytopic uncertainties, the robust stabilization problem is studied as well. Finally, a numerical example is given to show the validity of the proposed method.

关键词： Markovian jump systems time delay state feedback stabilization

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