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IEEE International geoscience and Remote Sensing Symposium

作者： Xue Yang Kun Fu Hao Sun Xian Sun Menglong Yan Wenhui Diao Zhi Guo 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

Object detection has been playing a significant role in the field of remote sensing for a long time but it is still full of challenges. In this paper, we propose a novel detection framework based on rotational region convolution neural network to cope with the problem of non-maximum suppression in dense objects detection. The bounding boxes obtained by adopting our method is the minimum bounding rectangle of object with less redundant regions. Furthermore, we find the head direction of the object through prediction. There are three important changes to our framework over traditional detection methods, representation and regression of rotational bounding box, head direction prediction and rotational non-maximal suppression. Experiments based on remote sensing images from Google Earth for Object detection show that our detection method based on rotational region CNN has a competitive performance.

关键词： Object detection Rotating region Convolution neural network Prediction Non-maximal suppression

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An improved method to sort and pair TDOA based on the correlation between TDOAs

An improved method to sort and pair TDOA based on the correl...

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6th International Conference on Radar, RADAR 2011

作者： Ren, Wen-Juan Hu, Dong-Hui Ding, Chi-Biao Institute of Electronics Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing 100190 China Graduate School Chinese Academy of Sciences No. 19 North 4yh Ring Road West Haidian District Beijing 100190 China

ISBN: (纸本)9781424484416

In TDOA passive location system, the situations in which false TDOA combinations arose are sorted. For extracting the correct TDOA combination of the radiant source, an improved method to sort and pair TDOA is proposed in this paper. The method can eliminate the false TDOA combinations which are reduced by sub-PRI(pulse repetition interval) of the radiant source with high pulse repetition frequency(PRF) and the equality of multiple targets' TDOAs, and associate all TDOA combinations which belong to the same target. The method enhances the accuracy of extracting TDOA combination and reduces the computation load and the complexity of afterward processing. © 2011 IEEE.

关键词： Time difference of arrival

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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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RELAXATION LABELLING BASED LAND MASKING IN SAR IMAGES

RELAXATION LABELLING BASED LAND MASKING IN SAR IMAGES

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IEEE International geoscience and Remote Sensing Symposium

作者： Zongxu Pan Lei Liu Bin Lei Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing China

In this paper, a relaxation labelling based land masking method is proposed for separating sea and land in SAR images. Land masking, also known as sea-land segmentation, is a part of ship detection system for SAR images to avoid detecting false alarms in the land. Relaxation labeling is an iterative method, which can separate foreground pixels from background ones using the neighborhood information of pixels in the image. When relaxation labelling converges, the segmented result is often unsatisfactory, since it tends to label more foreground pixels. To overcome this issue, a loss composed of the background probability distribution diversity and the gradient magnitude of the result is introduced to indicate when to stop the iteration. Experimental results on several Gaofen-3 SAR images demonstrate the effectiveness of the proposed method.

关键词： Labeling Image segmentation Synthetic aperture radar Marine vehicles Probability distribution Morphology Iterative methods

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ANISOTROPIC SCATTERING DETECTION FOR CHARACTERIZING POLARIMETRIC CIRCULAR SAR MULTI-ASPECT SIGNATURES

ANISOTROPIC SCATTERING DETECTION FOR CHARACTERIZING POLARIME...

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IEEE International geoscience and Remote Sensing Symposium

作者： Yang Li Yun Lin Wen Hong Ran Xu Zhimin Zhuo Qiang Yin Beijing Institute of Electronic System Engineering Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Science Beijing China Beijing University of Chemical Technology Beijing China

Circular synthetic aperture radar (CSAR) can provide distinctive multi-aspect anisotropic scattering signatures. However, it is impossible to retain the anisotropic signatures in a SAR image that combines all the subapertures coherently or incoherently. In this letter, we propose a polarimetric CSAR anisotropic scattering detection framework to characterize multi-aspect and fully polarimetric SAR signatures of point-like and distributed targets. We applied this framework to quantify and rank media polarimetric scattering dissimilarity over all aspects and to determine whether the most different one shows anisotropy by use of constant false alarm rate (CFAR) detection. Furthermore, we demonstrated the monotonicity of CFAR detection function and incorporated this function to decrease the complexity of the anisotropic scattering test. Our algorithm was validated and applied to a set of airborne P-band fully polarimetric circular SAR data acquired by the Institute of Electronics, Chinese Academy of Science (IECAS). The results indicate the framework can retain anisotropic scattering and extract a series of new multi-aspect polarimetric SAR signatures for terrain classification.

关键词： Scattering Apertures Synthetic aperture radar Covariance matrices Radar polarimetry Image color analysis Optical imaging

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On-orbit geometric rectification for micro-satellite based on Lightweight feature database

On-orbit geometric rectification for micro-satellite based o...

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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Linhui Wang Yuming Xiang Feng Wang Yuxin Hu Hongjian You Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences School of Electronics Electrical and Communication Engineering University of Chinese Academy of Sciences

On-orbit processing is becoming more prevalent due to its ability to efficiently exploit satellite resources. On-orbit geometric rectification improves positioning accuracy for follow-up tasks such as object detection or geometric calibration, while avoiding heavy burden on downlinking bandwidth and time delay. However, existing rectification methods faces some challenges. The hardware resources onboard satellites are restricted, and geographic positioning is often inaccurate. In this article, we propose a novel method designed for on-orbit rectification. The proposed method introduces a two-step registration framework to overcome large initial offsets and also a feature-compressing strategy to reduce the storage space of reference patches. Quantitative and practical experiments demonstrate that the proposed method performs well in terms of storage space, time efficiency as well as registration accuracy.

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Influence of beam stepping control and pointing deviation on image quality of spaceborne sliding spotlight SAR 3

Influence of beam stepping control and pointing deviation on...

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3rd China International SAR Symposium, CISS 2022

作者： Zeng, Tian Li, Zheng Yang, Wei Men, Zhirong Beihang University School of Electronic and Information Engineering Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geospatial Information Processing and Application System Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350398717

ISBN: (纸本)9798350398717

Sliding spotlight mode is widely used in spaceborne SAR to achieve high resolution. However, due to the difficulty for antenna beam to scan continuously, it usually works by step scanning, which leads to paired-echo in SAR images. Meanwhile, the pointing of the antenna often deviates from the preset value when scanning, resulting in an increase in the amplitude of the paired echo. This paper works on the effect of beam stepping control and pointing deviation on image quality of Ka band spaceborne sliding spotlight SAR. Firstly, the azimuth echo signal in sliding spotlight mode under the beam stepping control and pointing deviation is modeled. Then, the influence of beam stepping control and pointing deviation is analyzed by using the paired-echo theory, and the quantitative relationship between beam granularity and pointing deviation and paired-echo is presented. Finally, the validity of the theoretical analysis is verified by computer simulation. The method in this paper provides a reliable basis for the design of parameters in spaceborne sliding spotlight SAR system. © 2022 IEEE.

关键词： Synthetic aperture radar

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Analysis of phase preservation and interferometric offset test in sparse SAR imaging

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Science China(information Sciences) 2024年第2期67卷 219-231页

作者： Zhongqiu XU Bingchen ZHANG Guangzuo LI Xueli ZHAN Yanfei BAO Yirong WU Key Laboratory of Spatial Information Processing and Application System Technology Chinese Academy of Sciences Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of Sciences PLA Academy of Military Sciences

Sparse synthetic aperture radar(SAR) imaging has emerged as a reliable microwave imaging scheme in the recent decade and excels in down-sampling reconstruction and full-sampling performance improvements such as noise, sidelobe, speckle, and ambiguity suppression. To utilize complex image products of sparse reconstruction for improvement in polarimetric, interferometric, and tomographic SAR imaging, it is necessary to evaluate the phase preservation of sparse SAR imaging. In this study, we first introduce the general alternating direction method of multipliers(ADMM) as the universal framework for sparse reconstruction algorithms and adopt chirp scaling algorithm(CSA)-based azimuth-range decouple operators to avoid expensive data storage and processing. Further, we theoretically analyze the phase preservation of the sparse reconstruction algorithm through a comparison with the reconstruction results of CSA. Finally,we conduct the interferometric offset test on the sparse reconstruction results of simulated and real Gaofen-3(GF-3) SAR data, demonstrating the phase-preserving ability of sparse methods.

关键词： sparse SAR imaging phase preservation interferometric offset test general ADMM azimuth-range decouple

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HIGH QUALITY REMOTE SENSING IMAGE SUPER-RESOLUTION USING DEEP MEMORY CONNECTED NETWORK

HIGH QUALITY REMOTE SENSING IMAGE SUPER-RESOLUTION USING DEE...

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IEEE International geoscience and Remote Sensing Symposium

作者： Wenjia Xu Guangluan Xu Yang Wang Xian Sun Daoyu Lin Yirong Wu 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

Single image super-resolution is an effective way to enhance the spatial resolution of remote sensing image, which is crucial for many applications such as target detection and image classification. However, existing methods based on the neural network usually have small receptive fields and ignore the image detail. We propose a novel method named deep memory connected network (DMCN) based on a convolutional neural network to reconstruct high-quality super-resolution images. We build local and global memory connections to combine image detail with environmental information. To further reduce parameters and ease time-consuming, we propose down-sampling units, shrinking the spatial size of feature maps. We test DMCN on three remote sensing datasets with different spatial resolution. Experimental results indicate that our method yields promising improvements in both accuracy and visual performance over the current state-of-the-art.

关键词： spatial resolution Remote sensing Image reconstruction Training Convolutional neural networks Satellites

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SAR Moving Target Segmentation and Removal Based on Deep Learning 8th

SAR Moving Target Segmentation and Removal Based on Deep L...

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8th EAI International Conference on Machine Learning and Intelligent Communications, MLICOM 2023

作者： Wu, Yifan Qi, Xiyu Huang, Lijia Zhang, Bingchen Yan, Lili Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100190 China China Centre for Resources Satellite Data and Application Beijing100094 China

ISBN: (纸本)9783031717154

Synthetic Aperture Radar (SAR) stands as an integral part of advanced remote sensing technology. Nevertheless, practical applications experience inevitable disturbances from moving target noise, compromising both image integrity and target detection performance. This paper introduces a pioneering approach reliant on deep learning principles for the elimination of moving target noise within SAR imaging. Firstly, we use the Back-Projection (BP) algorithm to form the foundational images from echo signals. Employing the Unet network, we subsequently acquire a segmentation map of the moving target noise. By subtracting this segmented noise map from the original image, we succeed in the effective erasure of moving targets, yielding a resultant image devoid of moving target noise. Experimental validation demonstrates that our method can effectively remove moving target noise and yield SAR images that solely contain static scenes. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Deep learning

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