检索结果-内蒙古大学图书馆

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

Image super-resolution quality assessment: Structural fidelity versus statistical naturalness

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arXiv 2021年

作者： Zhou, Wei Wang, Zhou Chen, Zhibo Dept. of Electrical and Computer Engineering University of Waterloo WaterlooONN2L3G1 Canada CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

Single image super-resolution (SISR) algorithms reconstruct high-resolution (HR) images with their low-resolution (LR) counterparts. It is desirable to develop image quality assessment (IQA) methods that can not only evaluate and compare SISR algorithms, but also guide their future development. In this paper, we assess the quality of SISR generated images in a two-dimensional (2D) space of structural fidelity versus statistical naturalness. This allows us to observe the behaviors of different SISR algorithms as a tradeoff in the 2D space. Specifically, SISR methods are traditionally designed to achieve high structural fidelity but often sacrifice statistical naturalness, while recent generative adversarial network (GAN) based algorithms tend to create more natural-looking results but lose significantly on structural fidelity. Furthermore, such a 2D evaluation can be easily fused to a scalar quality prediction. Interestingly, we find that a simple linear combination of a straightforward local structural fidelity and a global statistical naturalness measures produce surprisingly accurate predictions of SISR image quality when tested using public subject-rated SISR image datasets. Code of the proposed SFSN model is publicly available at https://***/weizhou-geek/SFSN. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

GRIDLESS TOMOGRAPHIC SAR IMAGING BASED ON ACCELERATED ATOMIC NORM MINIMIZATION WITH EFFICIENCY

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arXiv 2022年

作者： Gao, Silin Zhang, Zhe Zhang, Bingchen Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China

Synthetic aperture radar (SAR) tomography (TomoSAR) enables the reconstruction and three-dimensional (3D) localization of targets based on multiple two-dimensional (2D) observations of the same scene. The resolving along the elevation direction can be treated as a line spectrum estimation problem. However, traditional super-resolution spectrum estimation algorithms require multiple snapshots and uncorrelated targets. Meanwhile, as the most popular TomoSAR imaging method in modern years, compressed sensing (CS) based methods suffer from the gridding mismatch effect which markedly degrades the imaging performance. As a gridless CS approach, atomic norm minimization can avoid the gridding effect but requires enormous computing resources. Addressing the above issues, this paper proposes an improved fast ANM algorithm to TomoSAR elevation focusing by introducing the IVDST-ANM algorithm, which reduces the huge computational complexity of the conventional time-consuming semi-positive definite programming (SDP) by the iterative Vandermonde decomposition and shrinkage-thresholding (IVDST) approach, and retains the benefits of ANM in terms of gridless imaging and single snapshot recovery. We conducted experiments using simulated data to evaluate the performance of the proposed method, and reconstruction results of an urban area from the SARMV3D-Imaging 1.0 dataset are also presented. Copyright © 2022, The Authors. All rights reserved.

关键词： Compressed sensing

A novel TomoSAR imaging method with few observations based on nested array

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arXiv 2022年

作者： Jiang, Pengyu Zhang, Zhe Zhang, Bingchen Xu, Zhongqiu 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 Suzhou Aerospace Information Research Institute Suzhou215000 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou21500 China

Synthetic aperture radar tomography (TomoSAR) baseline optimization technique is capable of reducing system complexity and improving the temporal coherence of data, which has become an important research in the field of TomoSAR. In this paper, we propose a nested TomoSAR technique, which introduces the nested array into TomoSAR as the baseline configuration. This technique obtains uniform and continuous difference co-array through nested array to increase the degrees of freedom (DoF) of the system and expands the virtual aperture along the elevation direction. In order to make full use of the difference co-array, covariance matrix of the echo needs to be obtained. Therefore, we propose a TomoSAR sparse reconstruction algorithm based on nested array, which uses adaptive covariance matrix estimation to improve the estimation performance in complex scenes. We demonstrate the effectiveness of the proposed method through simulated and real data experiments. Compared with traditional TomoSAR and coprime TomoSAR, the imaging results of our proposed method have a better anti-noise performance and retain more image information. Copyright © 2022, The Authors. All rights reserved.

关键词： Covariance matrix

A Hybrid and Explainable Deep Learning Framework for SAR Images

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A Hybrid and Explainable Deep Learning Framework for SAR Ima...

IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Zhongling Huang Mihai Datcu Zongxu Pan Bin Lei Key Laboratory of Technology in Geo-spatial Information Processing and Application System CAS Remote Sensing Technology Institute (IMF) German Aerospace Center (DLR)

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Deep learning based patch-wise Synthetic Aperture Radar (SAR) image classification usually requires a large number of labeled data for training. Aiming at understanding SAR images with very limited annotation and taking full advantage of complex-valued SAR data, this paper proposes a general and practical framework for quad-, dual-, and single-polarized SAR data. In this framework, two important elements are taken into consideration: image representation and physical scattering properties. Firstly, a convolutional neural network is applied for SAR image representation. Based on time-frequency analysis and polarimetric decomposition, the scattering labels are extracted from complex SAR data with unsupervised deep learning. Then, a bag of scattering topics for a patch is obtained via topic modeling. By assuming that the generated scattering topics can be regarded as the abstract attributes of SAR images, we propose a soft constraint between scattering topics and image representations to refine the network. Finally, a classifier for land cover and land use semantic labels can be learned with only a few annotated samples. The framework is hybrid for the combination of deep neural network and explainable approaches. Experiments are conducted on Gaofen-3 complex SAR data and the results demonstrate the effectiveness of our proposed framework.

关键词： Deep learning Training Annotations Scattering Image representation Radar polarimetry Synthetic aperture radar

Improving resistance to adversarial deformations by regularizing gradients

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arXiv 2020年

作者： Xia, Pengfei Li, Bin CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application Systems Department of Electronic Engineering and Information Science University of Science of Technology of China Hefei China

Improving the resistance of deep neural networks against adversarial attacks is important for deploying models to realistic applications. Currently, most defense methods are designed to defend against additive noise attacks, their performance cannot be guaranteed when against non-additive noise attacks. In this paper, we focus on adversarial deformations, a typical class of non-additive noise attacks, and propose a flow gradient regularization with random start to improve the resistance of models. Theoretically, we prove that, compared with input gradient regularization, regularizing flow gradients is able to get a tighter bound. Across multiple datasets, architectures, and adversarial deformations, our experimental results consistently indicate that models trained with flow gradient regularization can acquire a better resistance than trained with input gradient regularization with a large margin. Moreover, compared with adversarial training, our method can achieve better results in optimization-based and gradient-free attacks, and combining these two methods can improve the resistance against deformation attacks further. Finally, we give a unified form of gradient regularization, which can be used to derive the corresponding form when facing other types of attack. Copyright © 2020, The Authors. All rights reserved.

关键词： Deformation

Multi-Aspect SAR Target Recognition Based on Efficientnet and GRU

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Multi-Aspect SAR Target Recognition Based on Efficientnet an...

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

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

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

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