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

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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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Interpreting the Latent Space of GANs via Correlation Analysis for Controllable Concept Manipulation

arXiv

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arXiv 2020年

作者： Li, Ziqiang Tao, Rentuo Niu, Hongjing Li, Bin CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application Systems University of Science and Technology of China Anhui China

Generative adversarial nets (GANs) have been successfully applied in many fields like image generation, inpainting, super-resolution and drug discovery, etc., by now, the inner process of GANs is far from been understood. To get deeper insight of the intrinsic mechanism of GANs, in this paper, a method for interpreting the latent space of GANs by analyzing the correlation between latent variables and the corresponding semantic contents in generated images is proposed. Unlike previous methods that focus on dissecting models via feature visualization, the emphasis of this work is put on the variables in latent space, i.e. how the latent variables affect the quantitative analysis of generated results. Given a pretrained GAN model with weights fixed, the latent variables are intervened to analyze their effect on the semantic content in generated images. A set of controlling latent variables can be derived for specific content generation, and the controllable semantic content manipulation be achieved. The proposed method is testified on the datasets Fashion-MNIST and UT Zappos50K, experiment results show its effectiveness. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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The effects of channel imbalances on the azimuth multi-channel SAR 5

The effects of channel imbalances on the azimuth multi-chann...

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IET International Radar Conference 2020, IET IRC 2020

作者： Shang, Mingyang Qiu, Xiaolan Han, Bing Yang, Junying Ding, Chibiao Zhong, Lihua Zhou, Xiao Aerospace Information Research Institute Chinese Academic of Sciences Beijing China Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China National Key Laboratory of Microwave Imaging Technology Beijing100190 China

ISBN: (纸本)9781839535635

Azimuth multi-channel (AMC) synthetic aperture radar (SAR) is an effective technology to conquer the minimum antenna area constraint and can provide high-resolution and wide-swath (HRWS) SAR images compared with the conventional strip-map SAR. In order to suppress azimuth ambiguity, AMC SAR will face an important problem: channel imbalances estimation and compensation. At first, this paper theoretically analysis the effects of channel imbalances including amplitude imbalance, phase imbalance and range sampling time imbalance (RSTI) on AMC SAR images, where all these channel imbalances will introduce false targets in azimuth, and RSTI will also make a significant influence on the range of SAR images. And then, in order to validate the above result, some simulations based on the parameters are carried out, and the real image acquired by GF-3 is also described. In addition, the intensity, number, and position of false targets are discussed. Finally, this paper makes some conclusions and points out the problems of these results. © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Synthetic aperture radar

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A method for position offset correction of ship target on SAR image based on AIS data 5

A method for position offset correction of ship target on SA...

引用

IET International Radar Conference 2020, IET IRC 2020

作者： Lv, Yini Zhong, Lihua Yang, Junying Qiu, Xiaolan Yuan, XinZhe Han, Bing Ding, Chibiao Aerospace Information Research Institute Chinese Academic of Sciences Beijing China Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China National Satellite Ocean Application of Service Beijing China

ISBN: (纸本)9781839535635

This paper proposes a method for the ship target azimuth offset correction on the Synthetic Aperture Radar (SAR) image using Automatic Identification System (AIS) data. Traditional AIS and SAR image ship matching usually use AIS position, size, and other information. This paper uses a continuous position sequence around the SAR image acquisition time, that is, the complete trajectory, to calculate the slant range between the ship and the space-borne SAR platform. By deriving the slant range, the radial velocity can be obtained. According to the radial velocity, the azimuth offset of the moving ship is estimated to correct the ship's position on the SAR image. © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Synthetic aperture radar

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The Research on the Space-Time Variation of Phase Imbalance for GF-3 Azimuth Multichannel Mode 6

The Research on the Space-Time Variation of Phase Imbalance ...

引用

6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Shang, Mingyang Qiu, Xiaolan Han, Bing Zhong, Lihua DIng, Chibiao Yang, Junying Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Institute of Electronics Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728129129

Azimuth multichannel (AMC) synthetic aperture radar (SAR) is an advanced technique which can prevent the minimum antenna area constraint and provide high-resolution and wide-swath (HRWS) SAR images. Channel imbalance calibration is an important topic in AMC SAR signal processing, since it has a great impact on the image quality. The phase imbalance of AMC SAR is not only related to the radar system, but also to the satellite attitude. This paper first models the relationship between phase imbalance and satellite attitude, and analyzes the influence of satellite attitude on the phase imbalance. In the presence of the satellite attitude deviation, the phase imbalance is time-variation in azimuth, and spatial-variation in range. Then, the real data acquired by Gaofen-3 (GF-3) in the ultra-fine strip-map (UFS) mode is used to verify the relationship model both in theoretical calculations and estimation. The result shows that the model proposed in this paper can describe the space-time variation of phase imbalance very accurately. Finally, some discussions and conclusions are drawn and the future research perspectives are proposed. © 2019 IEEE.

关键词： Synthetic aperture radar

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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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Decentralized and Lightweight Cross-Chain Transaction Scheme Based on Proxy Re-signature

Decentralized and Lightweight Cross-Chain Transaction Scheme...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Huiying Zou Jia Duan Xi Liu Wei Ren Tao Li Xianghan Zheng Kim-Kwang Raymond Choo School of Computer Science China University of Geosciences Wuhan China The Third Surveying and Mapping Institute of Hunan Province Hunan Engineering Research Center of Geographic Information Security and Application Hunan China Key Laboratory of Data Protection and Intelligent Management Sichuan University Ministry of Education China State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR CASM Beijing China College of Computer and Big Data Fuzhou University School of Information and Intelligent Engineering Sanya College China Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio USA

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

With the widespread application of digital assets and the rapid development of blockchain technology, achieving secure and efficient transactions between different blockchain networks has become an urgent challenge. Existing cross-chain methods impose limitations, e.g., hash lock technology exhibits low scalability, side-chain technology is overly complex, and notary schemes pose centralization risks. In order to address these issues, we propose a cross-chain transaction solution based on proxy re-signature technology. To tackle the centralization concerns, we employ proxy re-signature technology to decentralize the authority of notaries among transaction participants, minimizing the potential for centralization at a low cost. We further present an extended scheme that can guarantee more requirements such as higher transaction amount and shorter transaction time. Furthermore, we analyze and compare the signature technologies used in the proposed solution and provide a systematic proof of our approach’s security.

关键词： Privacy Systematics Costs Scalability Blockchains Security

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3-D context entropy model for improved practical image compression

arXiv

引用

arXiv 2020年

作者： Guo, Zongyu Wu, Yaojun Feng, Runsen Zhang, Zhizheng Chen, Zhibo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China

In this paper, we present our image compression framework designed for CLIC 2020 competition. Our method is based on Variational AutoEncoder (VAE) architecture which is strengthened with residual structures. In short, we make three noteworthy improvements here. First, we propose a 3-D context entropy model which can take advantage of known latent representation in current spatial locations for better entropy estimation. Second, a light-weighted residual structure is adopted for feature learning during entropy estimation. Finally, an effective training strategy is introduced for practical adaptation with different resolutions. Experiment results indicate our image compression method achieves 0.9775 MS-SSIM on CLIC validation set and 0.9809 MS-SSIM on test set. Copyright © 2020, The Authors. All rights reserved.

关键词： Image compression

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3-D Context Entropy Model for Improved Practical Image Compression

3-D Context Entropy Model for Improved Practical Image Compr...

引用

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Zongyu Guo Yaojun Wu Runsen Feng Zhizheng Zhang Zhibo Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

关键词： Context modeling Solid modeling Image coding Entropy Training Transforms Image resolution

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Learned Video Compression with Feature-level Residuals

Learned Video Compression with Feature-level Residuals

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Runsen Feng Yaojun Wu Zongyu Guo Zhizheng Zhang Zhibo Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

In this paper, we present an end-to-end video compression network for P-frame challenge on CLIC. We focus on deep neural network (DNN) based video compression, and improve the current frameworks from three aspects. First, we notice that pixel space residuals is sensitive to the prediction errors of optical flow based motion compensation. To suppress the relative influence, we propose to compress the residuals of image feature rather than the residuals of image pixels. Furthermore, we combine the advantages of both pixel-level and feature-level residual compression methods by model ensembling. Finally, we propose a step-by-step training strategy to improve the training efficiency of the whole framework. Experiment results indicate that our proposed method achieves 0.9968 MS-SSIM on CLIC validation set and 0.9967 MS-SSIM on test set.

关键词： Image coding Training Optical imaging Adaptive optics Motion compensation Optical distortion Decoding

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