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arXiv 2023年

作者： Feng, Hao Liu, Shaokai Deng, Jiajun Zhou, Wengang Li, Houqiang The 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 Zhangjiang Laboratory Shanghai China The University of Adelaide Australian Institute for Machine Learning Australia

In recent years, tremendous efforts have been made on document image rectification, but existing advanced algorithms are limited to processing restricted document images, i.e., the input images must incorporate a complete document. Once the captured image merely involves a local text region, its rectification quality is degraded and unsatisfactory. Our previously proposed DocTr, a transformer-assisted network for document image rectification, also suffers from this limitation. In this work, we present DocTr++, a novel unified framework for document image rectification, without any restrictions on the input distorted images. Our major technical improvements can be concluded in three aspects. Firstly, we upgrade the original architecture by adopting a hierarchical encoder-decoder structure for multi-scale representation extraction and parsing. Secondly, we reformulate the pixel-wise mapping relationship between the unrestricted distorted document images and the distortion-free counterparts. The obtained data is used to train our DocTr++ for unrestricted document image rectification. Thirdly, we contribute a real-world test set and metrics applicable for evaluating the rectification quality. To our best knowledge, this is the first learning-based method for the rectification of unrestricted document images. Extensive experiments are conducted, and the results demonstrate the effectiveness and superiority of our method. We hope our DocTr++ will serve as a strong baseline for generic document image rectification, prompting the further advancement and application of learning-based algorithms. The source code and the proposed dataset are publicly available at https://***/fh2019ustc/DocTr-Plus. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Doppler walk rectification based on KWT in passive radar

Doppler walk rectification based on KWT in passive radar

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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Xin Guan Li-Hua Zhong Dong-Hui Hu Chi-Biao Ding Key Laboratory of Technology in Geospatial Information Processing and Application System Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781509033331

Doppler walk is introduced by high speed motion of targets in passive radar, which decreases the signal-to-noise ratio. In this paper, a Doppler walk rectification method based on keystone-Wigner-Ville transform (KWT) is proposed, and the interference, weights and noise impact are analyzed. No prior information and parameters searching are unnecessary.

关键词： Doppler effect Interference Transforms Frequency modulation Object detection Passive radar Filtering

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A Novel Reconstruction Filter for the Azimuth Multi-Channel SAR

A Novel Reconstruction Filter for the Azimuth Multi-Channel ...

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IEEE International Conference on Radar

作者： Mingyang Shang Xiaolan Qiu Junying Yang Lihua Zhong Yu Xin Chibiao Ding Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Aerospace Information Research Institute Chinese Academic of Sciences Beijing China Beijing Institute of Remote Sensing Information Beijing China

ISBN: (纸本)9781665468893

The compensation of channel imbalances plays a vital role in signal processing of the azimuth multi-channel (AMC) synthetic aperture radar (SAR). In the operational AMC SAR system, the channel imbalance is usually considered to be a constant in both the time and frequency domain for one image. However, since the antenna patterns of all channels cannot be strictly consistent, the channel imbalances may vary with the Doppler frequency. This paper establishes the signal model of AMC SAR with channel imbalances changing with Doppler frequency and proposes a novel compensation method during the reconstruction for the echo signals received by all channels. This method has been verified by simulations and real data acquired by Gaofen-3 (GF-3). The result shows that this method has excellent potential for improving the image quality for AMC SAR.

关键词： Image quality Antenna measurements Azimuth Filter banks Receiving antennas Channel estimation Signal processing

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Combined analysis of probability of missed detection and positioning accuracy for receiver selection in SFN based passive radar systems

Combined analysis of probability of missed detection and pos...

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International Conference on Signal processing Proceedings (ICSP)

作者： Xiao-bo Li Xin Guan Li-hua Zhong Dong-hui Hu Chi-biao Ding Key Laboratory of Technology in Geospatial Information Processing and Application System Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781479921898

Multistatic passive radar systems provide higher positioning accuracy comparing with the bistatic ones. However, the positioning performance of the multistatic passive radar systems is largely dependent on the geometry. Especially, it is difficult to designate transmitters from the given ones in a single frequency network (SFN) based passive radar system. Therefore the receiver location is of crucial importance. In this paper, the influence of different receiver positions for the probability of missed detection and the target positioning accuracy are analyzed. Then a performance function for selecting the optimal receiver is proposed. Finally, a simulation with three transmitters and one receiver is presented.

关键词： Accuracy Passive radar Receiving antennas Radio transmitters

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Model selection for high resolution InSAR coherence statistics over urban areas and its application in building detection

Model selection for high resolution InSAR coherence statisti...

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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Yue Zhang Xian Sun Wenhui Diao Chenyuan Wang Guangluan Xu Hongqi Wang Institute of Electronic Chinese Academy of Sciences Beijing China Key Laboratory of Spatial Information Processing and Application System Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781509033331

The interferometric coherence map is derived from the cross-correlation of two registered synthetic aperture radar (SAR) images. It can give additional information complementary to the intensity image, or act as an independent information source in many applications. Compared to the plenty of work on SAR intensity statistics, there are quite fewer researches on the statistical characters of interferometric SAR (InSAR) coherence. And to our knowledge, all of the existing work that related to InSAR coherence statistics, models the coherence with Gaussian distribution with no discrimination on data resolutions or scene types. Our main contribution is the investigation on the accuracies of several typical models for high resolution coherence statistics over urban areas. We select three typical land classes including trees, buildings, and shadow, as the representatives of urban areas. And different models including Gaussian, Weibull, Rayleigh, Nakagami and Beta are evaluated. Experiment results on TanDEM-X data illustrate that the Beta model reveals a better performance than other distributions. Finally, the Beta model is used in the detection of buildings.

关键词： Coherence Buildings Synthetic aperture radar Urban areas Probability density function Data models Nakagami distribution

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A new method for attitude refinement of earth observation satellite with asynchronous images

A new method for attitude refinement of earth observation sa...

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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Tao Sun Li Huang Hui Long Bao-Cheng Liu Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing P. R. China TH-Centre of China Beijing P. R. China

The accuracy of attitude observation is always the main contributor to geometric performance of earth observation satellites (EOSs). Given the ground process requirement, linear pushbroom and asynchronous imaging sensors are widely used in EOSs, such as multispectral sensor with sequential line arrays, three-line array sensor in stereo mapping satellite, panchromatic sensor with multiple non-collinear CCD chip in some high-resolution optical satellite. By using the images of those sensors, this paper proposes a method, which is based on image registration approach, rigorous forward intersection and bundle adjustment technology, to refine attitude data of satellite for improving geometric performance of images. Preliminary experiments, which used multi-sensors asynchronous images of Chinese Mapping Satellite-1-02, demonstrate that the proposed method is capable of improving internally coincident precision of attitude data without ground control points. In particular, relative positioning accuracy of images can be directly improved, and absolute positioning accuracy can consequently be improved via additionally using a few GCPs in the stripe image data.

关键词： Satellites Sensor arrays Calibration Mathematical model Remote sensing Image sensors

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An ECG signal de-noising method combining the improved threshold function and ABC algorithm 3

An ECG signal de-noising method combining the improved thres...

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2022 3rd International Conference on Computer information and Big Data applications, CIBDA 2022

作者： Deng, Yanrong Huang, Yanhu School of Information and Communication Guilin University of Electronic Technology Guilin541004 China Guangxi Colleges and Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal University Yulin537000 China Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9783800758760

Electrocardiogram (ECG) plays an imperative role in the clinical diagnosis of heart disease and heart abnormalities. In order to obtain the original ECG signal morphology, the wavelet threshold technique is usually used for ECG signal de-noising, in which threshold function and thresholds play very important role. In this paper, an improved threshold function with adjustable shape parameter was structured to improve the problems of the constant deviation in soft threshold function and the discontinuousness in hard threshold function. And then, an optimization strategy based on artificial bee colony algorithm with mean square error (MSE) as the objective function is embedded into the process of de-noising to quickly determine the optimal shape parameter and thresholds. Subsequently, a series of the simulation experiment are carried out by using two benchmark signals and real ECG signal with different noise levels. The signal-to-noise ratio (SNR) and the MSE after de-noising are used as evaluation index of the proposed method. The evaluation results demonstrate that the improved threshold function shows a better performance than the traditional threshold functions under different noise levels, and the proposed method is effective for the selections of the optimal shape parameter and thresholds in ECG signal de-noising. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Optimization

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3D Point Cloud Reconstruction of Vehicle using Inversely Mapping and Voting from CSAR Images

3D Point Cloud Reconstruction of Vehicle using Inversely Map...

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2021 CIE International Conference on Radar, Radar 2021

作者： Feng, Shanshan Lin, Yun Teng, Fei Hong, Wen Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geospatial Information Processing and Application System University of Chinese Academy of Sciences Institute of Electronics Chinese Academy of Sciences Beijing China School of Electronic Information Engineering North China University of Technology Beijing China

ISBN: (纸本)9781665498142

3D reconstruction of object has raised much interest in the field of SAR. The feature of target at multi aspect angles can be obtained from sub-aperture images provided by circular SAR(CSAR), which is conducive to 3D reconstruction. Radargrammetric method is a conventional way to extract DEM while its performance for 3D reconstruction is limited by the image registration. It is difficult to find corresponding points in different sub-aperture images. In this paper, we proposed a method for 3D reconstruction of vehicle based on projections in sub-aperture images. According to the imaging mechanism of CSAR, different targets located on the same iso-range line in the zero doppler plane fall into the same cell. For a projection point, given a series of offsets, the projection point will be mapped inversely to the 3D mesh along the iso-range line. We can obtain candidates of the target. The intersection of iso-range lines can be regarded as voting process. For a candidate, the more times of intersection, the higher the number of votes, and the candidate point will be reserved. This fully excavates the information contained in the angle dimension of CSAR. The proposed approach is verified by the Gotcha Volumetric SAR Data Set. © 2021 IEEE.

关键词： Circular synthetic aperture radar(CSAR) Inversely mapping Iso-range line Three dimensional(3D) reconstruction Voting

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Two-Step Bistatic Spaceborne Sliding-Spotlight SAR Imaging Agorithm Based on Accurate Range Model

Two-Step Bistatic Spaceborne Sliding-Spotlight SAR Imaging A...

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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Jianbing Xiang Xiaolei Lv Xikai Fu Ye Yun University of Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

The slant range errors caused by traditional hyperbolic range equation (THRE) with stop-and-go assumption will lead to image defocusing in high resolution spaceborne sliding-spotlight Bistatic SAR system (ST-BiSAR). In this paper, an accurate bistatic slant range model based on uniform acceleration curve motion (UARM) is proposed, which is precisely fitted with the actual range history. Then, a two-step imaging algorithm based on UARM and method of reversion series (MSR) is introduced to eliminate aliasing phenomenon and realize focus. Finally, simulation results verify the correctness and effectives of the proposed range model and imaging algorithms.

关键词： Mathematical model Imaging Satellites Synthetic aperture radar Acceleration Manganese Azimuth

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HIGH RESOLUTION SAR IMAGE CLASSIFICATION WITH DEEPER CONVOLUTIONAL NEURAL NETWORK

HIGH RESOLUTION SAR IMAGE CLASSIFICATION WITH DEEPER CONVOLU...

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IEEE International geoscience and Remote Sensing Symposium

作者： Yue Zhang Xian Sun Hao Sun Zequn Zhang Wenhui Diao Kun Fu Key Laboratory of Spatial Information Processing and Application System Technology Chinese Academy of Sciences Beijing China Institute of Electronic Chinese Academy of Sciences Beijing China

Deeper architectures are proven to be beneficial for the classification performance obviously in computer vision field. Inspired by this, deep CNNs are expected to make progress in the SAR target classification problem as well. However, it is hard to train deeper CNNs for SAR images. Such CNNs have millions of parameters to be determined in the network (for example the VGGNet has more than 130 million parameters), hence large-scale dataset is indispensable when training a deep CNN. But there is no large-scale annotated SAR target dataset, and data acquisition and annotation is much more costly for SAR images. With inadequate data, the network is easy to be overfitting. Several methods based on deep learning have been proposed for SAR image classifications, but they cannot get rid of the aforementioned data limitation of labelled SAR images. To solve this problem, this paper proposes a microarchitecture called CompressUnit (CU). With CU, we design a deeper CNN. Compared with the network with the fewest parameters for SAR image classification in literature so far, our network is 2X deeper with only about 10% of parameters. In this way, we get a deeper network with much fewer parameters. This network is easier to be trained with limited SAR data and is more likely to get rid of overfitting.

关键词： Deeper CNN SAR images CompressUnit

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