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Preliminary Analysis of PolInSAR Scattering Mechanism in Urban Areas through UAV-borne PolInSAR System

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Preliminary Analysis of PolInSAR Scattering Mechanism in Urb...

IEEE International Conference on Radar

作者： Zexin Lv Xiaolan Qiu Chibiao Ding Key Laboratory of Technology in Geo-spatial Information Processing and Application System Beijing China Aerospace Information Research Institute (of Chinese Academy of Sciences) Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Aerospace Big Dara Application Technology Suzhou China Suzhou Aerospace Information Research Institute Suzhou China

ISBN: (纸本)9781665468893

Polarimetric Interferometric SAR (PolInSAR) can improve the coherence of images and it plays an important role in urban remote sensing. The explanation of its scattering mechanism is concerned by many researchers. It is always explained from the aspect of different polarization decompositions but seldom from the height of scattering centers. In this paper, a Ku-band UAV-borne PolInSAR system is used to compare the relationship of scattering centers between PolInSAR and Pauli decomposition on different artificial targets. The physical meaning of PolInSAR is preliminary analyzed and our conclusion is also verified through a C-band manned airborne PolInSAR system.

关键词： C-band Airborne radar Urban areas Scattering Coherence Radar polarimetry Synthetic aperture radar

spatial-temporal water area monitoring of Miyun Reservoir using remote sensing imagery from 1984 to 2020

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arXiv 2021年

作者： Liu, Chang Tang, Hairong Ji, Luyan Zhao, Yongchao Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Beijing100190 China

Miyun Reservoir has produced huge benefits in flood control, agricultural irrigation, power generation, aquaculture, tourism, and urban water supply. Accurately water mapping is of great significance to the ecological environment monitoring of the Miyun Reservoir and the management of the South-to-North Water Diversion Project. On the 60th anniversary of the completion of the Miyun Reservoir, we took the Miyun Reservoir as the study area and collected all the Landsat-5 and Landsat-8 remote sensing images from 1984 to 2020 for water mapping. Based on the spectral, topographical and temporal-spatial characteristics of water, we proposed an automated method for long-term researvoir mapping, which can solve the problems caused by cloud, shadow, ice and snow pixels. Moreover, it can also deal with the "same objects with different spectra" and spectral mixed problems. The overall accuracy is as high as 98.2% for the case with no cloud or snow/ice cover. The landscape division index is introduced to analyze the morphological changes of Miyun Reservoir. Based on the mapping results, it is suggested that the water area change of the reservoir can be divided into five periods: "growth period" (1984-1993), "peak period" (1994-1999), "decline period" (2000-2003), "protection period" "(2004-2014), and "recovery period" (2015-2020), during which the east and west areas of the reservoir have experienced the process of "division-combination-division-combination". The largest reservoir area reached 151.6 km2 (1995), and the smallest area was only 57.3 km2 (2004). Moreover, we also find that the largest area in a year occurs from August to September, and the smallest area occurs in May. In addition, we have found that the changes of water area of Miyun Reservoir are closely connected to urban development, government policies and precipitation varitations. The study shows that our method used here are potentially extendable to other reservoirs for long-term area monitoring. © 2021, CC

关键词： Time series analysis

A Method of Using Multichannel SAR spatial Phase information for Moving Target Positioning

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A Method of Using Multichannel SAR Spatial Phase Information...

IEEE International Conference on Radar

作者： Yini Lv Lihua Zhong Xiaolan Qiu Chibiao Ding Aerospace Information Research Institute Chinese Academy 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

ISBN: (纸本)9781665468893

Multichannel synthetic aperture radar (SAR) has the ability of high-resolution and wide-swath (HRWS) imaging. Therefore, it has become a vital means of marine surveillance. Moreover, the target positioning using SAR is essential in ship monitoring, and it is mainly concentrated on the image domain at present. This paper proposes a novel method of positioning the target using the spatial phase information of SAR echo without imaging. The multichannel SAR antenna can be regarded as an array composed of multiple elements, and based on the array signal processing theory, the target cone angle at the imaging moment can be obtained. Then, according to the range-Doppler equation and the geometric model of the earth, the target coordinates can be calculated with the cone angle known. Finally, the results of the simulation experiment illustrate the effectiveness of the proposed method.

关键词： Earth Array signal processing Surveillance geometric modeling Imaging Signal processing algorithms Radar imaging

Sasl: Saliency-adaptive sparsity learning for neural network acceleration

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arXiv 2020年

作者： Shi, Jun Xu, Jianfeng Tasaka, Kazuyuki Chen, Zhibo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China KDDI Research Japan

Accelerating the inference speed of CNNs is critical to their deployment in real-world applications. Among all the pruning approaches, those implementing a sparsity learning framework have shown to be effective as they learn and prune the models in an end-to-end data-driven manner. However, these works impose the same sparsity regularization on all filters indiscriminately, which can hardly result in an optimal structuresparse network. In this paper, we propose a Saliency-Adaptive Sparsity Learning (SASL) approach for further optimization. A novel and effective estimation of each filter, i.e., saliency, is designed, which is measured from two aspects: The importance for the prediction performance and the consumed computational resources. During sparsity learning, the regularization strength is adjusted according to the saliency, so our optimized format can better preserve the prediction performance while zeroing out more computation-heavy filters. The calculation for saliency introduces minimum overhead to the training process, which means our SASL is very efficient. During the pruning phase, in order to optimize the proposed data-dependent criterion, a hard sample mining strategy is utilized, which shows higher effectiveness and efficiency. Extensive experiments demonstrate the superior performance of our method. Notably, on ILSVRC-2012 dataset, our approach can reduce 49.7% FLOPs of ResNet-50 with very negligible 0.39% top-1 and 0.05% top-5 accuracy degradation. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

A Fusion processing Method for Satellite Detection Data by Beidou Short Message System 12th

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A Fusion Processing Method for Satellite Detection Data by B...

12th China Satellite Navigation Conference, CSNC 2021

作者： Li, Bing Yu, Xiao Sun, Xiaojuan Hu, Yuxin Wu, Haiyan Shi, Tao Chen, Ling School of Cyber Security University of Chinese Academy of Sciences Beijing100149 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Beijing Institute of Tracking and Telecommunications Technology Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100149 China National Space Science Center Chinese Academy of Science Beijing100190 China

ISBN: (纸本)9789811631375

With the support of Beidou terminal of short message service system, the space science satellite can transmit all-day data that are astronomical alert data such as gravitational waves and Gamma ray bursts and satellite telemetry parameters. Then Beidou message service system has become an important means for finding the space astronomical events quickly, planning the follow-up observation and monitoring the real-time health of satellite and payloads. Then the application fields of the Beidou system has expanded into space science observation and satellite emergency operation. The short message data receiving and processing in ground system has many features of near real-time and continuity which is different from the processing methods of satellite data transmitted by data channel and telemetry channel. And the multi-source satellite data of the telemetry parameters and the astronomical alert information from these three channels need to be integrated. So, the real-time processing system of satellite data based on Beidou short message transmission has great challenges. In this paper, we explored an organization method of Beidou short messages, and proposed a multi-source data fusion algorithm for the ground satellite data processing system. The data processing system with this method was designed and implemented. Applying to the ground system of the satellite named gravitational wave high-energy electromagnetic counterpart all-sky monitor, the performance analysis of improved system shows that the processing delay for important telemetry parameters and astronomical alert information is greatly shortened from average nine hours to several minutes. The Beidou short message service system shows great supports in real time observation of gamma ray bursts. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Satellites

TomoSAR-ALISTA: Efficient TomoSAR Imaging via Deep Unfolded Network

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arXiv 2022年

作者： Wang, Muhan Zhang, Zhe Wang, Yue Gao, Silin Qiu, Xiaolan Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Electrical and Computer Engineering Department George Mason University FairfaxVA22030 United States

Synthetic aperture radar (SAR) tomography (TomoSAR) has attracted remarkable interest for its ability in achieving three-dimensional reconstruction along the elevation direction from multiple observations. In recent years, compressed sensing (CS) technique has been introduced into TomoSAR considering for its super-resolution ability with limited samples. Whereas, the CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity and complex parameter fine-tuning. Among the different CS algorithms, iterative soft-thresholding algorithm (ISTA) is widely used as a robust reconstruction approach, however, the parameters in the ISTA algorithm are manually chosen, which usually requires a time-consuming fine-tuning process to achieve the best performance. Aiming at efficient TomoSAR imaging, a novel sparse unfolding network named analytic learned ISTA (ALISTA) is proposed towards the TomoSAR imaging problem in this paper, and the key parameters of ISTA are learned from training data via deep learning to avoid complex parameter fine-tuning and significantly relieves the training burden. In addition, experiments verify that it is feasible to use traditional CS algorithms as training labels, which provides a tangible supervised training method to achieve better 3D reconstruction performance even in the absence of labeled data in real applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Compressed sensing

THE REMOTE SENSING IMAGE geoMETRICAL MODEL of BP NEURAL NETWORK

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THE REMOTE SENSING IMAGE GEOMETRICAL MODEL of BP NEURAL NETW...

2020 International Conference on geomatics in the Big Data Era, ICGBD 2020

作者： Yue, C.Y. Sun, T. Xie, J.F. Beijing Institute of Space Mechanics and Electricity Beijing China Beijing Key Laboratory of Advanced Optical Remote Sensing Technology Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Land Satellite Remote Sensing Application Center Ministry of Natural Resources of P. R. China Beijing China

Imagery geometry models (IGMs) of the high-resolution satellite images (HRSIs) are always of great interest in the photogrammetry and remote sensing community for the raising new kinds of sensors and imaging systems. Especially the generalized sensor models (GSMs) have been widely used for positioning of satellite images, and the accuracy are already validated. Since Back propagation (BP) neural network is a better choice for the two key reasons of the replacement of physical sensor models by generalized sensor models, numerous mathematical estimations for every specialized sensor, and secret equations of the IGMs. Experiments are carried out to test the approximation accuracy of the new generalized sensor model. And the experimental results show that, the BP neural network is of extremely high accuracy for satellite imagery photogrammetric restitution. © 2020 C. Y. Yue et al.

关键词： Backpropagation

Article dgfnet: Dual gate fusion network for land cover classification in very high-resolution images

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Remote Sensing 2021年第18期13卷

作者： Guo, Yongjie Wang, Feng Xiang, Yuming You, Hongjian The Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

Deep convolutional neural networks (DCNNs) have been used to achieve state-of-the-art performance on land cover classification thanks to their outstanding nonlinear feature extraction abil-ity. DCNNs are usually designed as an encoder–decoder architecture for the land cover classification in very high-resolution (VHR) remote sensing images. The encoder captures semantic representation by stacking convolution layers and shrinking image spatial resolution, while the decoder restores the spatial information by an upsampling operation and combines it with different level features through a summation or skip connection. However, there is still a semantic gap between different-level features;a simple summation or skip connection will reduce the performance of land-cover classification. To overcome this problem, we propose a novel end-to-end network named Dual Gate Fusion Network (DGFNet) to restrain the impact of the semantic gap. In detail, the key of DGFNet consists of two main components: Feature Enhancement Module (FEM) and Dual Gate Fusion Module (DGFM). Firstly, the FEM combines local information with global contents and strengthens the feature representation in the encoder. Secondly, the DGFM is proposed to reduce the semantic gap between different level features, effectively fusing low-level spatial information and high-level semantic information in the decoder. Extensive experiments conducted on the LandCover dataset and the ISPRS Potsdam dataset proved the effectiveness of the proposed network. The DGFNet achieves state-of-art performance 88.87% MIoU on the LandCover dataset and 72.25% MIoU on the ISPRS Potsdam dataset. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： Convolution

The Z-eigenpairs of orthogonally diagonalizable symmetric tensors

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arXiv 2021年

作者： Wang, Lei Geng, Xiurui Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China University of the Chinese Academy of Sciences Beijing100049 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Science Beijing100190 China

In this paper, we focus on a special class of symmetric tensors, which can be orthogonally diagonalizable, and investigate their Z-eigenpairs problem. We show that the eigenpairs can be uniformly expressed using several basic eigenpairs, and the number of all the eigenpairs is uniquely determined by the order and rank of the symmetric tensor. In addition, we exploit the local optimality of each eigenpair by checking the second-order necessary condition. © 2021, CC BY.

关键词： Tensors