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Domain generalization via conditional invariant representations

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arXiv 2018年

作者： Li, Ya Gong, Mingming Tian, Xinmei Liu, Tongliang Tao, Dacheng CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems University of Science and Technology of China UBTECH Sydney Artificial Intelligence Institute SIT FEIT University of Sydney Department of Philosophy Carnegie Mellon University Department of Biomedical Informatics University of Pittsburgh

Domain generalization aims to apply knowledge gained from multiple labeled source domains to unseen target domains. The main difficulty comes from the dataset bias: Training data and test data have different distributions, and the training set contains heterogeneous samples from different distributions. Let X denote the features, and Y be the class labels. Existing domain generalization methods address the dataset bias problem by learning a domain-invariant representation h(X) that has the same marginal distribution (h(X)) across multiple source domains. The functional relationship encoded in (Y jX) is usually assumed to be stable across domains such that (Y jh(X)) is also invariant. However, it is unclear whether this assumption holds in practical problems. In this paper, we consider the general situation where both (X) and (Y jX) can change across all domains. We propose to learn a feature representation which has domain-invariant class conditional distributions (h(X)jY ). With the conditional invariant representation, the invariance of the joint distribution (h(X);Y ) can be guaranteed if the class prior (Y ) does not change across training and test domains. Extensive experiments on both synthetic and real data demonstrate the effectiveness of the proposed method. Copyright © 2018, The Authors. All rights reserved.

关键词： Statistical tests

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

Convolutional neural network-based block up-sampling for intra frame coding

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arXiv 2017年

作者： Li, Yue Liu, Dong Li, Houqiang Li, Li Wu, Feng Zhang, Hong Yang, Haitao CAS Key Laboratory of Technology in Geo-Spatial Information Processing Application System University of Science Technology of China Hefei230027 China University of Missouri-Kansas City 5100 Rockhill Road Kansas CityMO64111 United States Media Technology Laboratory Central Research Institute of Huawei Technologies Co. Ltd Shenzhen518129 China

—Inspired by the recent advances of image super-resolution using convolutional neural network (CNN), we propose a CNN-based block up-sampling scheme for intra frame coding. A block can be down-sampled before being compressed by normal intra coding, and then up-sampled to its original resolution. Different from previous studies on down/up-sampling-based coding, the up-sampling methods in our scheme have been designed by training CNN instead of hand-crafted. We explore a new CNN structure for up-sampling, which features deconvolution of feature maps, multi-scale fusion, and residue learning, making the network both compact and efficient. We also design different networks for the up-sampling of luma and chroma components, respectively, where the chroma up-sampling CNN utilizes the luma information to boost its performance. In addition, we design a two-stage up-sampling process, the first stage being within the block-by-block coding loop, and the second stage being performed on the entire frame, so as to refine block boundaries. We also empirically study how to set the coding parameters of down-sampled blocks for pursuing the frame-level rate-distortion optimization. Our proposed scheme is implemented into the High Efficiency Video Coding (HEVC) reference software, and a comprehensive set of experiments have been performed to evaluate our methods. Experimental results show that our scheme achieves significant bits saving compared with HEVC anchor especially at low bit rates, leading to on average 5.5% BD-rate reduction on common test sequences and on average 9.0% BD-rate reduction on ultra high definition (UHD) test sequences. Copyright © 2017, The Authors. All rights reserved.

关键词： Signal sampling

A parallel optimization model for massive data stream application

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Ruan Jian Xue Bao/Journal of Software 2009年第SUPPL. 1期20卷 23-33页

作者： Sun, Xiao-Juan Sun, Ning-Hui Lei, Bin Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Institute of Electronics The Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Computer System and Architecture Institute of Computing Technology The Chinese Academy of Sciences Beijing 100190 China

While computing is entering a new phase in which CPU improvements are driven by the addition of multiple cores on a single chip, rather than higher frequencies. Parallel processing on these systems is in a primitive stage, and requires the explicit use and knowledge of underlying thread architecture. Based on the features of massive data stream application, this paper proposes three-level pipelining programming model of multithreading system, which realizes the new synchronization mechanism with no contention of shared structures and is capable to provide differential service for data streams. Then the paper applies the new model to remote sensing information processing system and backbone network intrusion detection system, and evaluates the improved system on several multicore platforms. In performance analysis, the optimized effects of backbone network intrusion detection system are evaluated in several aspects of throughput scalability on both SPARC T1 and x86 platforms, the impacts of different multithreading mapping methods on throughput, and the comparison of response time and service quality before and after optimization. The experimental results show that the system throughput has good scalability on both platforms, the values of response time are greatly improved and the prioritized streams achieve better response time with the differential service mechanism. © by Institute of Software, the Chinese Academy of Sciences. All rights reserved.

关键词： Metadata

A Robust Super-resolution Gridless Imaging Framework for UAV-borne SAR Tomography

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arXiv 2024年

作者： Gao, Silin Wang, Wenlong Wang, Muhan Zhang, Zhe Yang, Zai Qiu, Xiaolan Zhang, Bingchen Wu, Yirong Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Mathematics & Statistics Xi’an Jiaotong University Xi’an710049 China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou Aerospace Information Research Institute Suzhou215123 China China National Key Laboratory of Microwave Imaging Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China

Synthetic aperture radar (SAR) tomography (TomoSAR) retrieves three-dimensional (3-D) information from multiple SAR images, effectively addresses the layover problem, and has become pivotal in urban mapping. Unmanned aerial vehicle (UAV) has gained popularity as a TomoSAR platform, offering distinct advantages such as the ability to achieve 3-D imaging in a single flight, cost-effectiveness, rapid deployment, and flexible trajectory planning. The evolution of compressed sensing (CS) has led to the widespread adoption of sparse reconstruction techniques in TomoSAR signal processing, with a focus on 1 norm regularization and other grid-based CS methods. However, the discretization of illuminated scene along elevation introduces modeling errors, resulting in reduced reconstruction accuracy, known as the "off-grid" effect. Recent advancements have introduced gridless CS algorithms to mitigate this issue. This paper presents an innovative gridless 3-D imaging framework tailored for UAV-borne TomoSAR. Capitalizing on the pulse repetition frequency (PRF) redundancy inherent in slow UAV platforms, a multiple measurement vectors (MMV) model is constructed to enhance noise immunity without compromising azimuth-range resolution. Given the sparsely placed array elements due to mounting platform constraints, an atomic norm soft thresholding algorithm is proposed for partially observed MMV, offering gridless reconstruction capability and super-resolution. An efficient alternative optimization algorithm is also employed to enhance computational efficiency. Validation of the proposed framework is achieved through computer simulations and flight experiments, affirming its efficacy in UAV-borne TomoSAR applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Cost effectiveness

A nonlocal interferometric phase filtering strategy for practical InSAR system

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A nonlocal interferometric phase filtering strategy for prac...

IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Xue Lin Dongsheng Fang Fangfang Li Ke Wang Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China School of Information Science and Engineering University of Jinan Jinan China Institute of Electronics Chinese Academy of Sciences Beijing China 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 School of Information Science and Engineering Henan University of Technology Zhengzhou China

Nonlocal interferometric phase filtering methods achieve excellent performance in both noise reduction and texture preservation, even in the case of complicated topography and low coherence. The main limitation of the nonlocal methods is the computational burden. This paper proposed a nonlocal phase filtering strategy for the practical InSAR system, which combine the nonlocal algorithm with the traditional method to improve the efficiency.

关键词： Filtering Coherence Noise reduction Frequency estimation Estimation Algorithm design and analysis Surfaces

DEEP SEMANTIC SEGMENTATION OF AERIAL IMAGERY BASED ON MULTI-MODAL DATA

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DEEP SEMANTIC SEGMENTATION OF AERIAL IMAGERY BASED ON MULTI-...

IEEE International geoscience and Remote Sensing Symposium

作者： Kaiqiang Chen Kun Fu Xian Sun Michael Weinmann Stefan Hinz Boris Jutzi Martin Weinmann Key Laboratory of Spatial Information Processing and Application System Technology Chinese Academy of Sciences Beijing P.R. China Institute of Computer Science II University of Bonn Bonn Germany Institute of Photogrammetry and Remote Sensing Karlsruhe Institute of Technology Karlsruhe Germany

In this paper, we focus on the use of multi-modal data to achieve a semantic segmentation of aerial imagery. Thereby, the multi-modal data is composed of a true orthophoto, the Digital Surface Model (DSM) and further representations derived from these. Taking data of different modalities separately and in combination as input to a Residual Shuffling Convolutional Neural Network (RSCNN), we analyze their value for the classification task given with a benchmark dataset. The derived results reveal an improvement if different types of geometric features extracted from the DSM are used in addition to the true orthophoto.

关键词： Semantics Image segmentation Feature extraction Labeling Vegetation mapping Buildings Benchmark testing

Efficient Gridless DoA Estimation Method of Non-uniform Linear Arrays with applications in Automotive Radars

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arXiv 2023年

作者： Gao, Silin Zhang, Zhe Wang, Muhan Zhang, Yan Zhao, Jie Zhang, Bingchen Wang, Yue Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou Aerospace Information Research Institute Jiangsu Suzhou China The Beijing Autoroad Tech Co. Ltd China Electrical and Computer Engineering Department George Mason University FairfaxVA United States

This paper focuses on the gridless direction-of-arrival (DoA) estimation for data acquired by non-uniform linear arrays (NLAs) in automotive applications. Atomic norm minimization (ANM) is a promising gridless sparse recovery algorithm under the Toeplitz model and solved by convex relaxation, thus it is only applicable to uniform linear arrays (ULAs) with array manifolds having a Vandermonde structure. In automotive applications, it is essential to apply the gridless DoA estimation to NLAs with arbitrary geometry with efficiency. In this paper, a fast ANM-based gridless DoA estimation algorithm for NLAs is proposed, which employs the array manifold separation technique and the accelerated proximal gradient (APG) technique, making it applicable to NLAs without losing of efficiency. Simulation and measurement experiments on automotive multiple-input multiple-outpt (MIMO) radars demonstrate the superiority of the proposed method. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency

A new method for image understanding and retrieval using text-mined knowledge

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Lecture Notes in Computer Science (including subseries Lectu...

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8933卷 684-694页

作者： Tian, Jing Huang, Tinglei Huang, Y. Zhang, Z. Guo, Zhi Fu, Kun Key Laboratory of Technology in Geo-spatial Information Processing and Application Systems Institute of Electronics Chinese Academy of Sciences No.19 North 4th Ring Road West Haidian District Beijing100190 China University of Chinese Academy of Sciences No.19A Yuquan Road Shijingshan District Beijing100049 China

Existing approaches for image annotation generally demand training data with exact image labels or human-generated tags, which are often difficult to obtain. In this paper we present a novel model that utilizes the rich surrounding text of images to perform image annotation. Our work makes two main contributions. First, by integrating the state-of-the-art text analysis methods, words that describe the salient objects in images are extracted. Second, a new probabilistic topic model is built to jointly model image features, extracted words and surrounding text. Our model is demonstrated to be flexible enough to handle multi-modal features and provide better annotation prediction performance than the baseline model proposed in previous research. © Springer International Publishing Switzerland 2014.

关键词： Image annotation