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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Linlin Fang Lihua Zhong Bing Han Yuxin Hu Aerospace Information Research Institute Chinese Academy of Sciences Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Space-based radar has better coverage and is not limited by the curvature of the earth. Aircraft target tracking is particularly important in space-based radar system. However, the long observation distance and strong nonlinear observation increases the challenge of target tracking. Therefore, we proposed a space-based radar nonlinear tracking method for air targets based on variational Bayesian probability hypothesis density (VB-PHD) filter. The relationship between state and observation is established. Then, the VB method is adopted to deal with uncertainty of measurement noise variances. Besides, the cubature filter is introduced to deal with the strongly nonlinear models, and the recursion process is completed by using the third-order spherical radial volume rule. Finally, simulation scenarios with different measurement noise parameters were constructed, and the tracking result comparisons show that it effectively solves nonlinear problems, and tracking precision can be significantly improved.

关键词： Solid modeling Target tracking Uncertainty Spaceborne radar Simulation Noise Measurement uncertainty Radar tracking Bayes methods Noise measurement

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A Novel Gradient Descent Least Squares (GDLS) Algorithm for Efficient SMV Gridless Line Spectrum Estimation with applications in Tomographic SAR Imaging

arXiv

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arXiv 2022年

作者： Shi, Ruizhe Zhang, Zhe Qiu, Xiaolan Ding, Chibiao School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100190 China Suzhou Aerospace Information Research Institute Suzhou Key Laboratory of Intelligent Aerospace Big Data Application Technology Jiangsu Suzhou215123 China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Beijing100190 China

This paper presents a novel efficient method for gridless line spectrum estimation problem with single snapshot, namely the gradient descent least squares (GDLS) method. Conventional single snapshot (a.k.a. single measure vector or SMV) line spectrum estimation methods either rely on smoothing techniques that sacrifice the array aperture, or adopt the sparsity constraint and utilize compressed sensing (CS) method by defining prior grids and resulting in the off-grid problem. Recently emerged atomic norm minimization (ANM) methods achieved gridless SMV line spectrum estimation, but its computational complexity is extremely high;thus it is practically infeasible in real applications with large problem scales. Our proposed GDLS method reformulates the line spectrum estimations problem into a least squares (LS) estimation problem and solves the corresponding objective function via gradient descent algorithm in an iterative fashion with efficiency. The convergence guarantee, computational complexity, as well as performance analysis are discussed in this paper. Numerical simulations and real data experiments show that the proposed GDLS algorithm outperforms the state-of-the-art methods e.g., CS and ANM, in terms of estimation performances. It can completely avoid the off-grid problem, and its computational complexity is significantly lower than ANM. Our method has been tested in tomographic SAR (TomoSAR) imaging applications via simulated and real experiment data. Results show great potential of the proposed method in terms of better cloud point performance and eliminating the gridding effect. Copyright © 2022, The Authors. All rights reserved.

关键词： Spectrum analysis

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Plug-And-Play TomoSAR Imaging With Prior information

Plug-And-Play TomoSAR Imaging With Prior Information

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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Kun Wang Jie Li Yizhe Fan Bingchen Zhang Aerospace Information Research Institute Chinese Academy of Sciences 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 Beijing China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Synthetic aperture radar (SAR) tomography (TomoSAR) is an advanced remote sensing technology that has the ability to acquire three-dimensional information of targets. To enhance target features, different reaularization terms, including L 1 norm, total variation norm, and morphology term, have been introduced into the TomoSAR inversion model. However, there is a lack of attention to establishing a TomoSAR imaging framework with a modular structure that provides flexibility in specifically enhancing different features. In this article, a novel 3D tomographic reconstruction framework based on plug-and-play (PnP) priors and Alternating Direction Method of Multipliers (ADMM) is proposed. The PnP-ADMM framework achieves flexibility through the selection of appropriate priors for the features of targets, leading to a trade-off between the performance of feature enhancement and computational complexity. Simulation and real data experiments verify the effectiveness of the proposed method in flexibly selecting priors for feature enhancement of specific targets.

关键词： Radar remote sensing Three-dimensional displays Morphology Tomography Apertures Convex functions Computational complexity

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ROAD EXTRACTION FROM REMOTE SENSING IMAGES BY MULTIPLE FEATURE PYRAMID NETWORK

ROAD EXTRACTION FROM REMOTE SENSING IMAGES BY MULTIPLE FEATU...

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IEEE International geoscience and Remote Sensing Symposium

作者： Xun Gao Xian Sun Menglong Yan Hao Sun Kun Fu Yue Zhang Zhipeng Ge School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China

Road extraction from high-resolution remote sensing images has been applied in many domains, but it is still full of challenges. We focus on the problem of slender roads, proposing a new multiple feature pyramid network (MFPN), which is composed of an effective feature pyramid and the tailored pyramid pooling module based on PSPNet. These two designs can address the sparsity of roads in remote sensing images via using multi-level semantic features. Experiments on remote sensing images from Quick Bird show that our MFPN model achieves competitive performance, especially for slender roads.

关键词： Roads Feature extraction Remote sensing Convolution Semantics Image segmentation Data mining

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An Improved Imaging Method for Highly-Squinted SAR Based on Hyper-Optimized Admm

An Improved Imaging Method for Highly-Squinted SAR Based on ...

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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Tiancheng Chen Yang Meng Guoru Zhou Zhe Zhang Bingchen Zhang Yirong Wu Aerospace Information Research Institute Chinese Academy of Sciences 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 Beijing China

Highly-squinted synthetic aperture radar (SAR) echo has the characteristic of severe range-azimuth coupling, requiring specialized imaging algorithms. applications of compressed sensing in SAR imaging can effectively improve the resolution and other indicators. However, inaccurate manual parameters can affect the algorithm output. This article proposes an improved alternating direction method of multipliers (ADMM) for solving sparse reconstruction models under highly-squinted conditions. By adaptively adjusting the penalty parameter in ADMM via hyper-gradient descent (HD), the problem caused by inaccurate manual parameter is solved. Compared with matched filtering methods and other optimization methods, this method can suppress noise and speed up convergence. The effectiveness of the proposed method can be validated through the approximate observation of both simulated scenes and real scenes captured by the GF-3 SAR satellite.

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SFDFNet: Single-path Feature Deep Fusion Network for Fast Semantic Segmentation of SAR Images in Building Areas

SFDFNet: Single-path Feature Deep Fusion Network for Fast Se...

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IET International Radar Conference 2023, IRC 2023

作者： Zhang, Wenyi Dai, Xiangyu Chu, Qingwei Zhang, Jiande Ding, Hao Li, Guangzuo Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences No. 9 Dengzhuang South Road Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences No. 19 Yuquan Road Beijing China QiLu Aerospace Information Research Institute No. 44 Industry North Road Jinan China

ISBN: (纸本)9781839539954

With the SAR satellites have gradually become one of the most important methods of Earth observation, rapid interpretation of SAR images has become particularly important. However, the unique imaging mechanism of SAR makes its interpretation very difficult. In this paper, we propose the SFDFNet, a deep learning-based fast segmentation method for SAR image buildings, to address the problem of difficult SAR image interpretation. A bottleneck layer of buffered large receptive field is designed in the downsampling process. The unique design of the number of channels reduces the amount of computation without affecting the extraction of information. Meanwhile, improving on the ECA module, a two-layer ECA module was proposed to increase the receptive field and improve the linear expression. Experiments show that the proposed network has excellent results in segmentation accuracy and inference speed. © The Institution of Engineering & technology 2023.

关键词： Semantic Segmentation

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Recent Advance in Content-based Image Retrieval: A literature survey

arXiv

引用

arXiv 2017年

作者： Zhou, Wengang Li, Houqiang Fellow, Tian I.E.E.E. 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 Department of Computer Science University of Texas San Antonio San AntonioTX78249 United States

—The explosive increase and ubiquitous accessibility of visual data on the Web have led to the prosperity of research activity in image search or retrieval. With the ignorance of visual content as a ranking clue, methods with text search techniques for visual retrieval may suffer inconsistency between the text words and visual content. Content-based image retrieval (CBIR), which makes use of the representation of visual content to identify relevant images, has attracted sustained attention in recent two decades. Such a problem is challenging due to the intention gap and the semantic gap problems. Numerous techniques have been developed for content-based image retrieval in the last decade. The purpose of this paper is to categorize and evaluate those algorithms proposed during the period of 2003 to 2016. We conclude with several promising directions for future research. Copyright © 2017, The Authors. All rights reserved.

关键词： Content based retrieval

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DENSE-ADD NET: AN NOVEL CONVOLUTIONAL NEURAL NETWORK FOR REMOTE SENSING IMAGE INPAINTING

DENSE-ADD NET: AN NOVEL CONVOLUTIONAL NEURAL NETWORK FOR REM...

引用

IEEE International geoscience and Remote Sensing Symposium

作者： Daoyu Lin Guangluan Xu Yang Wang Xian Sun Kun Fu School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China

Through the recent performance of convolutional neural networks in image processing tasks, we propose a deep fully convolutional network for remote sensing image inpainting. The proposed Dense-Add Net (Dense-Add Network) can alleviate the vanishing-gradient problem, strengthen feature reuse, and substantially reduce the memory usage. We apply residual learning to learn the mappings from corrupted image to recovered image directly;it will back-propagate gradient to the bottom layers and accelerate the training process. We train the proposed Dense-Add Net with a robust Charbonnier loss function which can achieve high-quality reconstruction. The experimental verify the efficacy of our proposed Dense-Add Net.

关键词： Remote sensing Training Acceleration Image reconstruction Painting Convolution Interpolation

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A Novel Multi-Channel Phase Error Estimation Method Based On Stochastic Optimization For Tomographic Sar Autofocusing

A Novel Multi-Channel Phase Error Estimation Method Based On...

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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Muhan Wang Silin Gao Zhe Zhang Xiaolan Qiu Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou Aerospace Information Research Institute Suzhou China University of Chinese Academy of Sciences Beijing China

Tomographic SAR (TomoSAR) technology has gained significant attention in recent years due to its three-dimensional imaging capability. However, in practical applications, phase errors between different channels can degrade the quality of three-dimensional imaging. Current state-of-the-art methods for phase error compensation based on autofocus techniques suffer from high computational complexity, making them unsuitable for large-scale three-dimensional imaging. In this paper, we propose a multi-channel phase error estimation method based on error back-propagation training optimization. By utilizing the TomoSAR model that incorporates phase errors from multiple channels, we construct a matrix containing the parameters to be estimated for inter-channel phase errors. Through stochastic gradient descent algorithm, we iteratively optimize the parameters of the phase error matrix, ultimately obtaining an estimation of the inter-channel phase errors. Experimental results validate the accuracy of the proposed method.

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A visually inspired variational method for automatic image registration

引用

Cehui Xuebao/Acta geodaetica et Cartographica Sinica 2015年第8期44卷 893-899页

作者： Wang, Huixian Jin, Huijia Lei, Chengqiang Jiang, Wanshou Wang, Yan Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China Electronic & Optical Engineering Department Mechanical Engineering College Shijiazhuang050003 China Institute of Information in Remote Sensing in Beijing Beijing100192 China

A visually inspired variational method for automatic image registration is proposed to solve local deformation which traditional global registration model cannot well satisfy. The variational model considers local transformation, global smoothness and visual constraints. To account for intensity variations, we incorporate change of local contrast and brightness into our model. Firstly, the data entry of registration model is built according to the root-mean-square error of intensity;secondly, adaptive constraint using H1 half norm is used to ensure the global smooth in the model;finally, in order to make sure that the spatial attributes of the image satisfy the visual requirements and without distortion, the linear features are used as priori constraints. During the solution of model parameters, the whole image is used to globally estimate the transformation parameters, and then local estimation of the parameters is taken in a small neighbor. The entire procedure is built upon a multi-level differential framework, and the transformation parameters are calculated iteratively, which can consider both global smoothness and local distortion. To assess the quality of the proposed method, ZY-3 satellite images were used. Visual and quantitative analysis proved that the proposed method can significantly improve the registration precision. ©, 2015, SinoMaps Press. All right reserved.

关键词： Image registration

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