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1st China Digital Earth Conference, CDEC 2019

作者： Danpu, Zhao Xianqing, Tai Zhijie, Ma Key Laboratory of Technology in Geospatial Information Processing and Application System Institute of Elec-tronics Chinese Academy of Sciences Beijing100190 China Research Center of Intelligent City and Intelligent Transportation CAS R and D Center for Internet of Things Wuxi214135 China

Nowadays, the digital earth not only relates to the technologies of surveying and mapping geography, but also includes the analysis and cross-application of various scientific data related to geographic information. It requires a computing environment and management for the corresponding thematic application models which supports the online analysis on the digital earth. So we proposed an integrated deployment method for digital earth thematic application models based on container cloud, which provides a flexible and scalable information integration and analysis architecture for digital earth. Firstly, a Docker image building module is designed to facilitate the user to quickly build the application model into a image. Then the Docker technology is used to implement the containerization of the application model to provide a stable running environment for the application model. Finally, the Kubernetes is used to dynamically manage the cluster resources to realize rapid expansion of computing resources. In addition, the HDFS is deployed, and the image files and various scientific data respectively store with Hbase and Apache file systems. It can ensure the read and write speed of the application model and the related data and log files. It also used zabbix framework to manage the monitoring index of the container cloud cluster to ensure the execution effect of the application model. Through the tests of typical application model, it is verified that the method can integrated deploy and apply the cross-domain, hetero-architecture, multi-version and multi-class application models on the digital earth. © Published under licence by IOP Publishing Ltd.

关键词： Containers

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

arXiv

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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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TomoSAR-ALISTA: Efficient TomoSAR imaging via deep unfolded network

TomoSAR-ALISTA: Efficient TomoSAR imaging via deep unfolded ...

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International Conference on Radar systems (RADAR 2022)

作者： M. Wang Z. Zhang Y. Wang S. Gao X. Qiu Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing 100190 People's Republic of China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou 215123 People's Republic of China Electrical and Computer Engineering Department George Mason University Fairfax VA 22030 USA

ISBN: (数字)9781839537776

Synthetic aperture radar (SAR) tomography (TomoSAR) has attracted remarkable interest for its ability in achieving threedimensional 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 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.

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An attention-based word-level interaction model: Relation detection for knowledge base question answering

arXiv

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arXiv 2018年

作者： Zhang, Hongzhi Xu, Guandong Liang, Xiao Huang, Tinglei Fu, Kun CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China School of Software and Advanced Analytics University of Technology Sydney Australia

Relation detection plays a crucial role in Knowledge Base Question Answering (KBQA) because of the high variance of relation expression in the question. Traditional deep learning methods follow an encoding-comparing paradigm, where the question and the candidate relation are represented as vectors to compare their semantic similarity. Max- or average- pooling operation, which compresses the sequence of words into fixed-dimensional vectors, becomes the bottleneck of information. In this paper, we propose to learn attention-based word-level interactions between questions and relations to alleviate the bottleneck issue. Similar to the traditional models, the question and relation are firstly represented as sequences of vectors. Then, instead of merging the sequence into a single vector with pooling operation, soft alignments between words from the question and the relation are learned. The aligned words are subsequently compared with the convolutional neural network (CNN) and the comparison results are merged finally. Through performing the comparison on low-level representations, the attention-based word-level interaction model (ABWIM) relieves the information loss issue caused by merging the sequence into a fixed-dimensional vector before the comparison. The experimental results of relation detection on both SimpleQuestions and WebQuestions datasets show that ABWIM achieves state-of-the-art accuracy, demonstrating its effectiveness. Copyright © 2018, The Authors. All rights reserved.

关键词： Knowledge based systems

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Automatic Extraction of Supraglacial Lake using SAR Imagery and Deep Learning

Automatic Extraction of Supraglacial Lake using SAR Imagery ...

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IEEE International Conference on Radar

作者： Jiang Di Li Xinwu Gong Chen Hong Wen Wu Yirong Aerospace Information Research Institute Chinese Academy of Sciences 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 Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665468893

Supraglacial lake plays an important role in ice sheet dynamics, mass balance and sea level rise. Therefore, it is of great importance to extract supraglacial lake and obtain its spatial-temporal distribution or change. This study provides an automatic extraction model for supraglacial lake, using Synthetic Aperture Radar (SAR) imagery based on deep learning. First, select 19580 Sentinel-1 SAR imagery patches in eight typical areas for manual labeling. Second, the GPU-based U-Net model is used to implement the training of the supraglacial lake, and the results are evaluated in different sites. Finally, the training model is used to perform the supraglacial lake extraction. In addition, this article also introduces ArcticDEM to remove shadow confusion in the margin of the ice sheet. The global-local threshold segmentation method is used to extract the supraglacial lake on the Sentinel-2 MSI imagery, which is a comparative analysis and information supplement for the extracted results in this paper. The results show that: (1) The U-Net network selected in this paper is suitable for processing small sample size SAR data and multi-modal feature extraction. The GPU parallel processing method can achieve rapid extraction of massive data and reduce time cost. (2) The Dice coefficient of the training model reaches 0.98, which can be used for effective extraction of supraglacial lake. (3) Compared with the results of optical image extraction, the algorithm proposed in this paper can identify lakes in areas covered by snow or thin ice, which truly reflects the supraglacial lake temporal and spatial distribution characteristics.

关键词： Training Deep learning Snow Lakes Feature extraction Prediction algorithms Radar polarimetry

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

arXiv

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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

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Integrating Regularization and PnP Priors for SAR Image Reconstruction Using Multi-agent Consensus Equilibrium

Integrating Regularization and PnP Priors for SAR Image Reco...

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International Workshop on Compressed Sensing Theory and its applications to Radar, Sonar and Remote Sensing (CoSeRa)

作者： Yizhe Fan Mingqian Liu Mingxiao Shao Bingchen Zhang Yirong Wu Chinese Academy of Sciences Aerospace Information Research Institute Beijing China 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 Aeronautical Remote Sensing Center Chinese Academy of Sciences

ISBN: (数字)9798350365504

ISBN: (纸本)9798350365511

Multi-agent consensus equilibrium mechanism is a generalization of popular used PnP-ADMM method and composite regularization in computational sensing. We propose a novel SAR image sparse reconstruction method based on MACE mechanism. High-quality SAR images necessitate low ambiguity, high SNR, and low coherent speckle. We therefore integrate regularization priors and PnP priors for azimuth ambiguity suppression, sparsity inducing and despeckling. The proposed method’s performance is evaluated through experiments on both simulated and QILU-1 satellite SAR data.

关键词： TV Azimuth Spaceborne radar Satellite broadcasting Reconstruction algorithms Radar imaging Speckle Radar polarimetry Image reconstruction Signal to noise ratio

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A Pylon Detection Method Based on Faster R-CNN in High-Resolution SAR Images

A Pylon Detection Method Based on Faster R-CNN in High-Resol...

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Asian and Pacific Conference on Synthetic Aperture Radar (APSAR)

作者： Lv Xiaoling Jiankun Chen Xiaolan Qiu Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Aerospace Information Research Institute University of Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728173344

SyntheticAperture Radar (SAR) is an active microwave imaging system, which can provide all-time and allweather imaging with a wide observation range. In sub-meter high-resolution SAR images, man-made metallic targets, such as pylons, can be easily detected and their details can be obtained. Therefore, pylon detection using high-resolution SAR images has its unique advantages. To solve the problem that traditional pylon detection methods in SAR images require the manual design of feature extractors and perform poorly in terms of realtime requirements, an effective and fast framework for pylon detection in SAR images is proposed in this paper. It combines Faster R-CNN and sliding window algorithm to detect pylons in large-scene SAR images. Besides, it effectively improves detection accuracy by using data augmentation. The proposed method can detect up to 97.6% on the test set and 80% in largescene GF-3 SAR images. The average time cost of detection is 0. 06s for a slice image and 90s for a large-scale SAR image. Experiments demonstrate good performance in detection rate and instantaneity.

关键词： Costs Poles and towers Manuals Radar imaging Feature extraction Microwave theory and techniques Radar polarimetry

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MACHINE LEARNING-BASED MULTI-CHANNEL EVALUATION POOLING STRATEGY FOR IMAGE QUALITY ASSESSMENT

MACHINE LEARNING-BASED MULTI-CHANNEL EVALUATION POOLING STRA...

引用

IEEE International Conference on Image processing

作者： Anzhou Hu Rong Zhang Dong Yin Wenlong Hu Department of Electronic Engineering and Information Science University of Science and Technology of China Key Laboratory of Geospatial Information Processing and Application System Technology Chinese Academy of Sciences

ISBN: (纸本)9781479923427

Multi-channel peculiarity is one of the most widely accepted human visual system (HVS) models for perceptual image quality assessment (IQA). Otherwise than extensive studies of channel decomposition and intra-channel distortion measure, relatively scant research effort has been devoted to develop efficient multichannel evaluation pooling strategies. In this paper, we review and address the limitations of the conventional pooling models based on HVS sensitivities-weighted average. Instead, we explore the utilization of machine learning for this pooling problem, since machine learning can establish an optimal and generalized mapping that models the highly complex relationship between the multi-channel distortion evaluations and the perceived image quality. Experiments based on available subjective IQA databases demonstrate the rationality, reliability and robustness of our proposed scheme.

关键词： Image quality assessment (IQA) Multi-channel model Evaluation pooling Machine learning

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Real-time correlation tracking via joint model compression and transfer

arXiv

引用

arXiv 2019年

作者： Wang, Ning Zhou, Wengang Song, Yibing Ma, Chao Li, Houqiang 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 Hefei China Tencent AI Lab Shenzhen China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

Correlation filters (CF) have received considerable attention in visual tracking because of their computational efficiency. Leveraging deep features via off-the-shelf CNN models (e.g., VGG), CF trackers achieve state-of-the-art performance while consuming a large number of computing resources. This limits deep CF trackers to be deployed to many mobile platforms on which only a single-core CPU is available. In this paper, we propose to jointly compress and transfer off-the-shelf CNN models within a knowledge distillation framework. We formulate a CNN model pretrained from the image classification task as a teacher network, and distill this teacher network into a lightweight student network as the feature extractor to speed up CF trackers. In the distillation process, we propose a fidelity loss to enable the student network to maintain the representation capability of the teacher network. Meanwhile, we design a tracking loss to adapt the objective of the student network from object recognition to visual tracking. The distillation process is performed offline on multiple layers and adaptively updates the student network using a background-aware online learning scheme. Extensive experiments on five challenging datasets demonstrate that the lightweight student network accelerates the speed of state-of-the-art deep CF trackers to real-time on a single-core CPU while maintaining almost the same tracking accuracy. Copyright © 2019, The Authors. All rights reserved.

关键词： Object recognition

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