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Parameterization, sensitivity, and uncertainty of 1-D thermodynamic thin-ice thickness retrieval

Acta Oceanologica Sinica 2024年第7期43卷 93-111页

作者： Tianyu Zhang Mohammed Shokr Zhida Zhang Fengming Hui Xiao Cheng Zhilun Zhang Jiechen Zhao Chunlei Mi School of Geospatial Engineering and Science Sun Yat-Sen University/Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)Zhuhai 519082China Key Laboratory of Comprehensive Observation of Polar Environment(Sun Yat-Sen University) Ministry of EducationZhuhai 519082China State Key Laboratory of Remote Sensing Science College of Global Change and Earth System ScienceBeijing Normal UniversityBeijing 100875China Science and Technology Branch Environment and Climate Change CanadaToronto M3H5T4Canada Qingdao Innovation and Development Base(Centre) Harbin Engineering UniversityQingdao 266500China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China

Retrieval of Thin-Ice Thickness(TIT)using thermodynamic modeling is sensitive to the parameterization of the independent variables(coded in the model)and the uncertainty of the measured input *** article examines the deviation of the classical model’s TIT output when using different parameterization schemes and the sensitivity of the output to the ice ***,it estimates the uncertainty of the output in response to the uncertainties of the input *** parameterized independent variables include atmospheric longwave emissivity,air density,specific heat of air,latent heat of ice,conductivity of ice,snow depth,and snow *** input parameters include air temperature,ice surface temperature,and wind *** the independent variables,the results show that the highest deviation is caused by adjusting the parameterization of snow conductivity and depth,followed ice *** sensitivity of the output TIT to ice thickness is highest when using parameterization of ice conductivity,atmospheric emissivity,and snow conductivity and *** retrieved TIT obtained using each parameterization scheme is validated using in situ measurements and satellite-retrieved *** in situ measurements,the uncertainties of the measured air temperature and surface temperature are found to be *** resulting uncertainties of TIT are evaluated using perturbations of the input data selected based on the probability distribution of the measurement *** results show that the overall uncertainty of TIT to air temperature,surface temperature,and wind speed uncertainty is around 0.09 m,0.049 m,and−0.005 m,respectively.

关键词： Arctic sea ice 1-D thermodynamic ice model thin-ice thickness sea ice parameterization

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Improved YOLOX-S Marine Oil Spill Detection Based on SAR Images 12

Improved YOLOX-S Marine Oil Spill Detection Based on SAR Ima...

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12th International Conference on Information science and technology, ICIST 2022

作者： Zhang, Shuai Xing, Jun Wang, Xinzhe Fan, Jianchao School of Information Science and Engineering Dalian Polytechnic University Dalian China National Marine Environmental Monitoring Center Department of Marine Remote Sensing Technology Dalian China

ISBN: (数字)9781665485821

ISBN: (纸本)9781665485821

Marine oil spill spreads rapidly and has a long-term impact. Once it occurs, it will cause severe damage to the ecological environment. Synthetic Aperture Radar (SAR) is widely used in marine oil spill monitoring due to its all-weather and all-day characteristics. However, the contrast of different SAR images is inconsistent, making it difficult for the network to learn valuable features. To address this issue, this paper proposes an improved YOLOX-S (IYOLOX-S) model for marine oil spill detection. The model enhances image contrast by a truncated linear stretch module, uses CspDarknet and PANnet to extract image features, and obtains oil spill detection results through Decoupled Head. First, a truncated linear stretching module is added, which can improve the image contrast. It also highlights the characteristics of oil spill areas to enhance the networks learning ability. Second, the proposed score loss into the global loss function enhances the learning ability of the model and improves the detection accuracy. Experiments are carried out on the collected oil spill dataset, and the test sets average precision (AP) is 90.02%. The experimental results show that the improved YOLOX-S model accurately identifies oil spill areas. © 2022 IEEE.

关键词： Synthetic aperture radar

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Geospatial delineation of groundwater recharge potential zones in the Deccan basaltic province, India

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Arabian Journal of Geosciences 2023年第4期16卷 1-22页

作者： Saha, Rajarshi Wankhede, Tushar Das, Iswar Chandra Kumaranchat, Vinod Kumar Reddy, Satyanarayana Kurry Geosciences Group National Remote Sensing Centre ISRO Hyderabad India Department of Geology College of Science & Technology Andhra University Visakhapatnam India

The water-scarce drought-prone Manjeera basin, of the Deccan basaltic province, is facing the relentless threat of groundwater over-exploitation. This region needs a reliable strategy to overcome groundwater scarcity as this province is of great hydrogeological significance. There is a deficiency of dependable information about the groundwater recharge potential zone (GWRPZ) for this province. Groundwater recharge (GWR) is vital for creating a balance in water resources. With the aim of solving this severe water crisis, this study was conducted. This study is a very potent hybrid approach of satellite imagery and digital elevation model along with other ancillary data analysis using ArcGIS software geospatial platform with multi-criteria decision analysis (MCDA) driven analytical hierarchy process (AHP) and multi-influencing factor (MIF) models for GWRPZ map delineation. Crucial hydrogeological variables were utilized to achieve an efficient GWRPZ map for effective artificial recharge planning. The derived maps were classified into four distinct zones, viz. very good, good, moderate and poor. Critical parameters like branching factor (BF) (0.16 and 0.12), miss factor (MF) (0.11 and 0.26), detection percentage (DP) (89.86% and 79.17%) and overall quality percentage (OQP) (78.48% and 72.15%) demonstrated good prediction accuracy for AHP and MIF respectively. This research output may assist the planners/policymakers/local administration in precise groundwater development and recharge strategies to achieve a sustainable groundwater development scenario.

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A lightweight distillation CNN-transformer architecture for remote sensing image super-resolution

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International Journal of Digital Earth 2023年第1期16卷 3560-3579页

作者： Yu Wang Zhenfeng Shao Tao Lu Lifeng Liu Xiao Huang Jiaming Wang Kui Jiang Kangli Zeng State Key Laboratory for Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanPeople’s Republic of China School of General Aviation Jingchu University of TechnologyJingmenPeople’s Republic of China Hubei Key Laboratory of Intelligent Robot Wuhan Institute of TechnologyWuhanPeople’s Republic of China Department of Engineering University of DurhamDurhamUK Department of Environmental Sciences Emory UniversityAtlant1GAUSA School of Computer Science and Technology Harbin Institute of TechnologyHarbinPeople’s Republic of China NERCMS School of Computer ScienceWuhan UniversityWuhanPeople’s Republic of China

remote sensing images exhibit rich texture features and strong *** the super-resolution(SR)method of remote sensing images based on convolutional neural networks(CNN)can capture rich local information,the limited perceptual field prevents it from establishing long-distance dependence on global information,leading to the low accuracy of remote sensing image ***,it is difficult for existing SR methods to be deployed in mobile devices due to their large network parameters and high computational *** this study,we propose a lightweight distillation CNN-Transformer SR architecture,named DCTA,for remote sensing SR,addressing the aforementioned ***,the proposed DCTA first extracts the coarse features through the coarse feature extraction layer and then learns the deep features of remote sensing at different scales by fusing the feature distillation extraction module of CNN and *** addition,we introduce the feature fusion module at the end of the feature distillation extraction module to control the information propagation,aiming to select the informative components for better feature *** extracted low-resolution(LR)feature maps are reorganized through the up-sampling module to obtain high-resolution(HR)feature maps with high accuracy to generate highquality HR remote sensing *** experiments comparing different methods demonstrate that the proposed approach performs well on multiple datasets,including NWPU-RESISC45,Draper,and UC *** is achieved by balancing reconstruction performance and network complexity,resulting in both competitive subjective and objective results.

关键词： Super-resolution remote sensing lightweight network CNN-Transformer

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SAR image water extraction using the attention U-net and multi-scale level set method:flood monitoring in South China in 2020 as a test case

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Geo-Spatial Information science 2022年第2期25卷 155-168页

作者： Chuan Xu Shanshan Zhang Bofei Zhao Chang Liu Haigang Sui Wei Yang Liye Mei School of Computer Science Hubei University of TechnologyWuhanChina Geographic Information Center GuangZhou Urban Planning&Design Survey Research InstituteGuangzhouChina State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina School of Information Science and Engineering Wuchang Shouyi UniversityWuhanChina The Institute of Technological Sciences Wuhan UniversityWuhanChina

Level set method has been extensively used for image segmentation,which is a key technology of water ***,one of the problems of the level-set method is how to find the appropriate initial surface parameters,which will affect the accuracy and speed of level set ***,the semantic segmentation based on deep learning has opened the exciting research *** addition,the Convolutional Neural Network(CNN)has shown a strong feature representation ***,in this paper,the CNN method is used to obtain the initial SAR image segmentation map to provide deep a priori information for the zero-level set curve,which only needs to describe the general outline of the water body,rather than the accurate *** with the traditional circular and rectangular zero-level set initialization method,this method can converge to the edge of the water body faster and more precisely;it will not fall into the local minimum value and be able to obtain accurate segmentation *** effectiveness of the proposed method is demonstrated by the experimental results of flood disaster monitoring in South China in 2020.

关键词： Water extraction flood monitoring level set attention U-net Convolutional Neural Network(CNN)

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Development of Low-Cost 3D Building Model Using National Digital Elevation Model in Urban Area of Bandung City, Indonesia 1

Development of Low-Cost 3D Building Model Using National Dig...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Ihsan, K.T.N. Sakti, A.D. Safitri, S. Wikantika, K. Remote Sensing and Geographic Information Science Research Group Faculty of Earth Sciences and Technology Institut Teknologi Bandung Bandung40132 Indonesia Center for Remote Sensing Institut Teknologi Bandung Bandung40132 Indonesia Research Center for Sustainable Ecology and Geospatial Bandung40265 Indonesia

ISBN: (纸本)9781607685395

Sustainable city planning is one of many countries' 2030 sustainable development goals. In carrying out sustainable urban planning, it is necessary to look at the 3-dimensional aspect of the building. However, 3D mapping for large areas is very expensive. So we need an alternative to make three-dimensional buildings in urban areas that are cheaper. This study will review how well the 3-dimensional reconstruction of urban areas uses open-source data from the Indonesian National digital elevation model (DEMNAS) with a spatial resolution of 8 meters. This study found that the RMSE value of the DEMNAS 3D model reached 2.320 meters with the optimum 3-dimensional reconstruction at a medium building height around 5-7 meters. 3-dimensional modelling with DEMNAS will be good in relatively flat areas. This research hope can help better and lower cost sustainable city planning. © The Electrochemical Society

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SPTU-Lite: An Efficient Auxiliary Diagnostic Approach Combining Spiking Neural Networks And Large Kernel Extractors For ECG Signals

SPTU-Lite: An Efficient Auxiliary Diagnostic Approach Combin...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chuxuan Shan Xiaohua Wang Hang Qi Qingxu Meng Jiabao Wang Weijiang Wang Yueting Shi School of Integrated Circuits and Electronics Beijing Institute of Technology Beijing China Science and Technology on Millimeter-Wave Laboratory Beijing Institute of Remote-Sensing Equipment Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Electrocardiogram (ECG) is essential for the early prevention and diagnosis of cardiovascular diseases. The rise of wearable ECG devices has facilitated high-precision heart rate detection, enabling early interventions. Advances in Artificial Neural Networks (ANNs), particularly One-Dimensional Convolutional Neural Networks (CNNs) and Transformers, have significantly improved ECG signal classification. However, their computational and parametric demands limit their application, especially in small-scale devices. Spiking Neural Networks (SNNs), which operate on an event-driven principle, offer a solution with their resource efficiency. In this study, we propose a U-shaped hybrid CNN-Transformer architecture combining Spiking Fully Connected (FC) Layers and large kernel extractor (LKE) modules, named Spiking TransU-Lite (SPTU-Lite). The LKE module enhances the network’s receptive field and improves parameter efficiency, reducing model size without significant precision loss. Additionally, spiking neurons replace the Fully Connected classification layer, further reducing the model’s complexity. Empirical results on the MIT-BIH dataset show that SPTU-Lite reduces parameters by approximately 95%, while maintaining an accuracy of 99.62% and a loss rate below 0.5%, demonstrating its potential for ECG analysis in resource-constrained environments.

关键词： Accuracy Heart beat Prevention and mitigation Neurons Pattern classification Spiking neural networks Electrocardiography Transformers Kernel Speech processing

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Robust H∞ Attitude Stabilization for the Cluster Takeover of Non-cooperative Flexible Spacecraft 1

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29th International Conference on Computational and Experimental Engineering and sciences, ICCES 2023

作者： Yue, Xiaokui Yang, Ziyu Yu, Hongyu Huang, Panxing Liu, Yuan National Key Laboratory of Aerospace Flight Dynamics School of Astronautics Northwestern Polytechnical University Xi’an710072 China Science and Technology on Space Intelligent Control Laboratory Beijing Institute of Control Engineering Beijing100094 China Beijing Institute of Remote Sensing Information Beijing100011 China

ISBN: (数字)9783031449475

ISBN: (纸本)9783031449468

In this paper, a cooperative control scheme based on the small spacecraft cluster is proposed for the takeover control of the failed flexible spacecraft in space. In order to overcome external disturbance, inertia uncertainty and controller gain perturbation, the controller spacecraft calculates the control torque which can stabilize the combined spacecraft in theory, and generates the desired control signal according to the robust H∞ non-fragile control law set in advance. Based on the communication topology, the actuator spacecraft restores the control signal according to the distributed control torque allocation scheme considering the saturation state of all reaction wheels, and implements it to achieve the attitude stabilization and vibration suppression of the combined spacecraft. Simulation results show that the distributed control torque allocation scheme can implement the desired control signal with relatively small error. Under the condition that the external disturbance, controller gain perturbation and inertia uncertainty coexist, the angle and angular velocity of the combined spacecraft can be stabilized quickly. Moreover, the vibration of the flexible appendages can also be suppressed effectively. During the whole takeover control process, the dynamic allocation scheme can always ensure that all reaction wheels of each actuator spacecraft never reach saturation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Flexible spacecraft

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TASK SPECIFIC PRETRAINING WITH NOISY LABELS FOR remote sensing IMAGE SEGMENTATION

arXiv

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arXiv 2024年

作者： Liu, Chenying Albrecht, Conrad M. Wang, Yi Zhu, Xiao Xiang Data Science in Earth Observation Technical University of Munich Germany Remote Sensing Technology Institute German Aerospace Center Germany Germany

Compared to supervised deep learning, self-supervision provides remote sensing a tool to reduce the amount of exact, human-crafted geospatial annotations. While image-level information for unsupervised pretraining efficiently works for various classification downstream tasks, the performance on pixel-level semantic segmentation lags behind in terms of model accuracy. On the contrary, many easily available label sources (e.g., automatic labeling tools and land cover land use products) exist, which can provide a large amount of noisy labels for segmentation model training. In this work, we propose to exploit noisy semantic segmentation maps for model pretraining. Our experiments provide insights on robustness per network layer. The transfer learning settings test the cases when the pretrained encoders are fine-tuned for different label classes and decoders. The results from two datasets indicate the effectiveness of task-specific supervised pretraining with noisy labels. Our findings pave new avenues to improved model accuracy and novel pretraining strategies for efficient remote sensing image segmentation. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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A Physics-Based Atmospheric Precipitable Water Vapor Retrieval Algorithm by Synchronizing Modis Near-Infrared and Thermal Infrared Measurements

SSRN

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SSRN 2024年

作者： Zhou, Shugui Cheng, Jie School of Geo-Science and Technology Zhengzhou University Zhengzhou450001 China Institute of Remote Sensing Science and Engineering Faculty of Geographical Science Beijing Normal University Beijing100875 China State Key Laboratory of Remote Sensing Science Jointly sponsored by Beijing Normal University Aerospace Information Research Institute Chinese Academy of Sciences Beijing100875 China

This study proposed an innovative joint inversion algorithm that synchronized Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared (NIR) and thermal infrared (TIR) radiance data for accurate estimates of clear-sky precipitable water vapor (PWV). The NIR-TIR joint inversion algorithm consists of three core parts: (1) the NIR radiative transfer equation was simplified by assuming that reflectance changes approximately linearly with wavelength within the 0.85-1.25 μm range, which facilitates the simulation of top-of-atmosphere (TOA) radiance of the NIR water vapor absorption channel without requiring explicit reflectance information;(2) partial derivatives of NIR-TIR TOA radiance with respect to the corresponding background fields were derived by applying the one-term variational theorem to the radiative transfer equation;(3) an optimization approach was utilized to adjust the background fields until the discrepancy between the simulated and observed NIR-TIR TOA radiances was minimized. Subsequently, the refined water vapor profile was integrated to derive the PWV. In situ measurements collected from the Suominet and AERONET networks across North America were utilized to validate the NIR-TIR joint inversion algorithm. Additionally, the MODIS NIR PWV product (MYD05) and TIR PWV product (MYD07) were validated using the same in situ measurements for purposes of comparison. Validation results indicated the bias and root mean square error (RMSE) of the NIR-TIR joint inversion algorithm were -0.55 mm and 2.08 mm, respectively, which are superior to those of the MODIS NIR and TIR PWV products. The bias and RMSE were 3.84 mm and 4.86 mm for MYD05, and 0.41 mm and 4.60 mm for MYD07. Overall, the NIR-TIR joint inversion algorithm can disentangle temperature and water vapor information from TIR observation and achieve the accurate retrieval of the clear-sky PWV. This study offers a potential way to generate comprehensive, long-term, high spatial resolution environm

关键词： Water vapor

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