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Urban remote sensing Joint Event

作者： Fahong Zhang Yilei Shi Zhitong Xiong Xiao Xiang Zhu Data Science in Earth Observation Technical University of Munich Munich Germany Remote Sensing Technology Technical University of Munich Munich Germany

Semantic segmentation of urban scenes is widely recognized as a fundamental and crucial task that is of great significance to the urban planning and urban management. When applying the semantic segmentation models to large-scale or unseen scenarios, a so-called "domain shift" generally exists between the source and the target domain, where we train and evaluate our models. To mitigate such a problem, domain adaptation leverages the unlabeled data in the target domain to align or fine tuned the model using the target statistics. In previous domain adaptation approaches based on self-training, only the output-level knowledge (e.g., pseudo labels) from the source model is used to guide the adaptation process. However, we notice that the "pseudo features" generated by the source model could be of greater potential for capturing the relationship between target domain samples. To this end, we seek to exploit feature-level relation among neighboring pixels in the target domain to structure the prediction of the domain adaptation model. Experiments on public ISPRS datasets verify that the proposed method can outperform previous unsupervised domain adaptation methods, and demonstrate the benefits of mining local feature relation.

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Temperature-compensated ocean pressure sensor based on dual Fabry-Perot interferometers by wavelength demodulation

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Optics Express 2025年第13期33卷 27809-27823页

作者： Hao Huang Jianping Sun Guangyao Wang Ting Li Yingfan Xiong Liquan Sun Songkui Chen Yuning Duan School of Physics Science and Engineering Tongji University Shanghai 200092 China Division of Thermophysics Metrology National Institute of Metrology Beijing 100029 China Technology Innovation Center of Metrology Technology of Infrared Remote Sensing State Administration for Market Regulation Beijing 100088 China

A dual Fabry-Perot interferometric fiber-optic pressure sensor with temperature compensation is proposed for high-precision measurement. By integrating two Fabry-Perot sensors within a specialized encapsulation structure to form a pressure measurement cavity and a temperature reference cavity, the system effectively decouples pressure and temperature effects on cavity length. The detection of pressure and temperature variations is achieved through cavity length demodulation of the acquired spectral data using a multi-extrema-tracing algorithm. The system exhibits a pressure sensitivity of -8.627 nm/MPa at 20 ℃ over the pressure range of 0 MPa – 10 MPa, with nonlinearity error and repeatability error equivalent to maximum pressure deviations of 5.085 Pa and 25.369 Pa, respectively. Additionally, the temperature sensitivities of the two sensors are 0.1848nm/°C and 0.2575 nm/°C, respectively. Based on the temperature responses of the sensors, temperature compensation and error correction are applied to enhance the precision of pressure measurement. With its outstanding linearity, stability, and precision, the system demonstrates great potential for dynamic pressure measurement in extreme ocean environments.

关键词： Fabry Perot interferometers Fiber Bragg grating sensors Fiber optic sensors Precision metrology Pressure sensors Temperature sensors

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Basis Pursuit Denoising via Recurrent Neural Network Applied to Super-resolving SAR Tomography

arXiv

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arXiv 2023年

作者： Qian, Kun Wang, Yuanyuan Jung, Peter Shi, Yilei Zhu, Xiao Xiang Data Science in Earth Observation Technical University of Munich Munich Germany Remote Sensing Technology Technical University of Munich Munich Germany

This paper has been accepted by IEEE TGRS. Copyright may be transferred without further notice. Finding sparse solutions of underdetermined linear systems commonly requires the solving of L1 regularized least squares minimization problem, which is also known as the basis pursuit denoising (BPDN). They are computationally expensive since they cannot be solved analytically. An emerging technique known as deep unrolling provided a good combination of the descriptive ability of neural networks, explainable, and computational efficiency for BPDN. Many unrolled neural networks for BPDN, e.g. learned iterative shrinkage thresholding algorithm and its variants, employ shrinkage functions to prune elements with small magnitude. Through experiments on synthetic aperture radar tomography (TomoSAR), we discover the shrinkage step leads to unavoidable information loss in the dynamics of networks and degrades the performance of the model. We propose a recurrent neural network (RNN) with novel sparse minimal gated units (SMGUs) to solve the information loss issue. The proposed RNN architecture with SMGUs benefits from incorporating historical information into optimization, and thus effectively preserves full information to the final output. Taking TomoSAR inversion as an example, extensive simulations demonstrated that the proposed RNN outperforms the state-of-the-art deep learning-based algorithm in terms of super-resolution power as well as generalization ability. It achieved 10% to 20% higher double scatterers detection rate and is less sensitive to phase and amplitude ratio difference between scatterers. Test on real TerraSAR-X spotlight images also shows high-quality 3-D reconstruction of test site. Copyright © 2023, The Authors. All rights reserved.

关键词： Tomography

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MIclust: A Clustering Algorithm for High Dimensional Data

MIclust: A Clustering Algorithm for High Dimensional Data

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Namita Jain Susmita Ghosh Emmett J. Ientilucci Department of Computer Science Jadavpur University Kolkata India Digital Imaging and Remote Sensing Laboratory Chester F. Carlson Center for Imaging Science Rochester Institute of Technology

This paper presents a method for identifying clusters in hyperspectral images. A new mutual information based similarity index named k-MIDI is proposed. This index, k-MIDI is used for hierarchical clustering of random samples from the data. The remaining observations are assigned to the clusters based on their nearest neighbours.

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Evaluation of Flight Procedure Based on QAR Data: A Case Study on Daocheng Yading Airport

Evaluation of Flight Procedure Based on QAR Data: A Case Stu...

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IEEE International Conference on Civil Aviation Safety and Information technology (ICCASIT)

作者： Guo Yu Xiaoyue Zhang Zhe Dong Binbin Lu School of Remote Sensing and Information Engineering Wuhan University Wuhan China China Academy of Civil Aviation Science and Technology Beijing China

The research of the flight procedure evaluation is essential for ensuring flight safety. With Daocheng Yading Airport as the research object, combined with its Quick Access Recorder (QAR) data, a flight procedure evaluation method, namely Analytic Hierarchy Process, Entropy Weight Method, and the Technique for Order Preference by Similarity to an Ideal Solution (AHP-EWM-TOPSIS), was proposed in this paper. The process specifically includes: firstly, a secondary index system is built and optimized by Delphi research; secondly, according to results of the investigation and QAR data of Daocheng Yading Airport, index weights are determined by the combination of AHP and EWM; again, the comprehensive evaluation of the model is completed by TOPSIS; finally, the evaluation of actual samples is applied to analyze the model. Results show that the method proposed in this paper has a certain degree of effectiveness and feasibility for practice.

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Fast digital filter for frequency discriminator

Fast digital filter for frequency discriminator

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Electronic technology, Communication and Information (ICETCI), IEEE International Conference on

作者： Xinkai Yao Wei Xin Yu Jin Meng Li Qi Guo Xueyan Li College of Information Science and Technology Beijing University of Chemical Technology Beijing China Key Laboratory of Airborne Geophysics and Remote Sensing Geology Ministry of Natural Resources China Aero Geophysical Survey & Remote Sensing Center for Natural Resources Beijing China

The frequency discriminator is a circuit to detect signal frequency, it is widely used in various high-precision transducer. Frequency discriminators in optically pumped magnetometers (OPM) require fast tracking of the magnetic resonance center frequency, in which a fast-digital filter is necessary. This paper designs a high-speed digital filter, and is verified in field programmable gate array (FPGA). The designed filter is composed of a cascaded integrator-comb (CIC) digital filter and infinite impulse response (IIR), the CIC filter is developed to filter out harmonics and reduce the sampling rate, IIR filter is designed to further filter out high-frequency signals and pick up direct-current(DC)information. Compared with traditional Butterworth filters, the designed IIR filter saves about 40% stabilization time with less resources.

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Research on Robust Airspace Anti-jamming technology in Spaceborne OFDM System 2

Research on Robust Airspace Anti-jamming Technology in Space...

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2nd International Conference on Electronic Information technology and Smart Agriculture, ICEITSA 2022

作者： Wang, Wei Liu, Yongqing Huang, Yin Han, Hangcheng Wu, Zhijie Beijing Institute of Technology School of Integrated Circuits and Electronics Beijing China Sport Engineering Research Center China Institute of Sport Science Beijing China Beijing Institute of Remote Sensing Equipment Beijing China

ISBN: (纸本)9781665464017

In recent years, our country's low-orbit satellite communication system has developed rapidly. As a mature waveform transmission technology, OFDM technology is widely used in low-orbit satellite communication systems with the advantages of outstanding resistance to inter-symbol interference, free spectrum and resource allocation, etc. However, for the spaceborne OFDM system, the transmission performance of the system will be seriously affected, whether it is in a civil scenario with unintentional interference or in a complex military confrontation environment with intentional interference. Anti-jamming capability is an important factor to ensure the security and stability of the system. Aiming at the partial frequency band interference problem in the OFDM system, this paper studies an effective robust spatial adaptive notch tracking anti-jamming algorithm. For the LCMV algorithm cannot effectively resist the interference under low interference signal ratio and the expected signal direction is deviated. The situation of beamforming, a robust beamforming algorithm of MVDR based on diagonal loading is proposed, which is modeled based on the unique area array antenna array, and the detailed flow of the algorithm is given. Through the simulation and verification of the algorithm, the proposed algorithm can effectively suppress the interference of some frequency bands. © 2022 IEEE.

关键词： Satellite communication systems

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Actuation and Control Strategies for Ultralightweight Active Thin Shell Curved Space Reflectors 9

Actuation and Control Strategies for Ultralightweight Active...

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9th Symposium on Novel Photoelectronic Detection technology and Applications

作者： Wang, Kainan Yu, Yian Preumont, André Institute of Aerospace Science and Technology School of Remote Sensing and Information Engineering Wuhan University Wuhan430072 China Hubei Luojia Laboratory Wuhan430072 China CP. 165-55 50 Av. F.D. Roosevelt BrusselsB-1050 Belgium

ISBN: (纸本)9781510664432

This paper proposes an ultra-lightweight design of space reflectors made of polymer thin layers, to increase the aperture size of space observation platform with higher imaging resolution. The morphing capabilities are evaluated with both petal-like segmented and monolithic configured reflectors as the primary mirror of space telescopes, by forming a set of Zernike modes. Finally, a compound control strategy using a deformable relay mirror to compensate the residual surface error corrected partially by the active unimorph primary mirror. © 2023 SPIE.

关键词： Mirrors

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Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems:A Review

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IEEE/CAA Journal of Automatica Sinica 2023年第6期10卷 1361-1387页

作者： Sibo Cheng César Quilodrán-Casas Said Ouala Alban Farchi Che Liu Pierre Tandeo Ronan Fablet Didier Lucor Bertrand Iooss Julien Brajard Dunhui Xiao Tijana Janjic Weiping Ding Yike Guo Alberto Carrassi Marc Bocquet Rossella Arcucci Data Science Institute Department of ComputingImperial College LondonSW72AZ London Department of Earth Science and Engineering Imperial College LondonSW72AZ London Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China the IMT Atlantique Lab-STICCUMR CNRS 6285France and OdysseyInria/IMTFrance.P.Tandeo is also with RIKEN Center for Computational ScienceKobeJapan the CEREA École des Ponts and EDF R&Dîle-de-FranceFrance the Laboratoire Interdisciplinaire des Sciences du Numérique CNRSParis-Saclay UniversityF-91403OrsayFrance the Electricitéde France(EDF) 78401 ChatouFranceInstitut de Mathématiques de Toulouse31062 ToulouseFrance and SINCLAIR AI LabSaclayFrance the Sorbonne University ParisFranceand also with Nansen Environmental and Remote Sensing Center(NERSC)BergenNorway the School of Mathematical Sciences Tongji UniversityShanghai 200092China the Mathematical Institute for Machine Learning and Data Science KU Eichstaett-IngolstadtBavariaGermany the School of Information Science and Technology Nantong UniversityNantong 226019China the Department of Physics and Astronomy“Augusto Righi” University of Bologna40124 BolognaItaly

Data assimilation(DA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal *** applications span from computational fluid dynamics(CFD)to geoscience and climate ***,much effort has been given in combining DA,UQ and machine learning(ML)*** research efforts seek to address some critical challenges in high-dimensional dynamical systems,including but not limited to dynamical system identification,reduced order surrogate modelling,error covariance specification and model error correction.A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains,resulting in the necessity for a comprehensive *** paper provides the first overview of state-of-the-art researches in this interdisciplinary field,covering a wide range of *** review is aimed at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models,but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their ***,this article has a special focus on how ML methods can overcome the existing limits of DA and UQ,and vice *** exciting perspectives of this rapidly developing research field are also *** Terms-Data assimilation(DA),deep learning,machine learning(ML),reduced-order-modelling,uncertainty quantification(UQ).

关键词： assimilation overcome apply

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Cross-Resolution Land Cover Classification Using Outdated Products and Transformers

arXiv

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arXiv 2024年

作者： Ni, Huan Zhao, Yubin Guan, Haiyan Jiang, Cheng Jie, Yongshi Wang, Xing Shen, Yiyang School of Remote Sensing & Geomatics Engineering Nanjing University of Information Science & Technology Nanjing210044 China Technology Innovation Center of Integration Applications in Remote Sensing and Navigation Ministry of Natural Resources Nanjing210044 China Jiangsu Engineering Center for Collaborative Navigation Positioning and Smart Applications Nanjing210044 China Beijing Key Laboratory of Advanced Optical Remote Sensing Technology Beijing Institute of Space Mechanics and Electricity Beijing100094 China

Large-scale high-resolution land cover classification is a prerequisite for constructing Earth system models and addressing ecological and resource issues. Advancements in satellite sensor technology have led to an improvement in spatial resolution and wider coverage areas. Nevertheless, the lack of high-resolution labeled data is still a challenge, hindering the large-scale application of land cover classification methods. In this paper, we propose a Transformer-based weakly supervised method for cross-resolution land cover classification using outdated data. First, to capture long-range dependencies without missing the fine-grained details of objects, we propose a U-Net-like Transformer based on a reverse difference mechanism (RDM) using dynamic sparse attention. Second, we propose an anti-noise loss calculation (ANLC) module based on optimal transport (OT). Anti-noise loss calculation identifies confident areas (CA) and vague areas (VA) based on the OT matrix, which relieves the impact of noises in outdated land cover products. By introducing a weakly supervised loss with weights and employing unsupervised loss, the RDM-based U-Net-like Transformer was trained. remote sensing images with 1 m resolution and the corresponding ground-truths of six states in the United States were employed to validate the performance of the proposed method. The experiments utilized outdated land cover products with 30 m resolution from 2013 as training labels, and produced land cover maps with 1 m resolution from 2017. The results show the superiority of the proposed method compared to state-of-the-art methods. The code is available at https://***/yu-ni1989/ANLC-Former. © 2024, CC BY-NC-ND.

关键词： Semantics

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