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14th European Conference on Synthetic Aperture Radar, EUSAR 2022

作者： Qian, Kun Wang, Yuanyuan Zhu, Xiaoxiang Data Science in Earth Observation Technical University of Munich Munich Germany Department of EO Data Science Remote Sensing Technology Institute German Aerospace Center Oberpfaffenhofen Germany

ISBN: (纸本)9783800758234

SAR tomography (TomoSAR) has been extensively applied in 3-D reconstruction in dense urban areas. Compressive sensing (CS)-based algorithms are generally considered as the state-of-The-Art methods in super-resolving TomoSAR, in particular in the single-look case. TomoSAR algorithms, including the CS-based ones, usually require a fairly large number of images to achieve a reliable reconstruction, because large error and especially bias occur in low number of measurements. In addition, CS-based algorithms are extremely computationally expensive due to their sparse reconstruction. These factors hinder their practical use. This paper demonstrates the potential of a novel and computationally efficient deep learning algorithm for TomoSAR on very small interferometric stacks. Investigation of the super-resolution ability shows that the proposed algorithm outperforms the state-of-The-Art CS-based TomoSAR algorithm by a fair margin when limited acquisitions are available. Test on real TanDEM-X data with 6 interferograms also shows high-quality 3-D reconstruction. © 2022 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Compressed sensing

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DEEP LEARNING-BASED APPROACH FOR ANNUAL CHANGE DETECTION AND OPEN PIT COAL MINE DETECTION USING SATELLITE IMAGERY 44

DEEP LEARNING-BASED APPROACH FOR ANNUAL CHANGE DETECTION AND...

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44th Asian Conference on remote sensing, ACRS 2023

作者： Prasetya, Koni Dwi Tsai, Fuan International PhD Program in Environmental Science and Technology National Central University 300 Jhongda Rd. Jhongli Taoyuan32001 Taiwan Center for Space and Remote Sensing Research National Central University 300 Jhongda Rd. Jhongli Taoyuan32001 Taiwan

ISBN: (纸本)9781713893646

This study introduces a novel deep learning-based method for detecting open pit coal mines in medium high (MH)resolution satellite imagery and analyzing annual variations in coal mining areas. The study aims to monitor and precisely identify coal mines, given their vital function as a primary energy source and a significant contributor to greenhouse gas emissions. The proposed method employs the U-Net architecture and ResNet 34 as its backbone for accurate detection and classification. The applied dataset consists of multispectral imagery from Sentinel-2 and synthetic aperture radar (SAR) imagery from Sentinel-1. Manual labelling of known coal mine locations using mining data as a reference, subdividing Sentinel image patches for training U-Net convolutional neural networks (CNNs) to classify coal mine and non-coal mine areas, and training and testing U-Net architecture and ResNet as a backbone deep learning model are the three essential steps involved in the process. The classification accuracy of the coal mining detection deep learning model is 97%, and the kappa value is 0.91. Preliminary results indicate that the investigation demonstrates the evolution of coal mining from 2016 to 2021, with an increase of over 40% in coal mining area in 2019. In 2017 and 2020, the area mined for coal will decrease. Variations in annual coal mining variations emphasize the significance of replanting efforts. The proposed method uses deep learning and satellite imagery to provide an accurate and efficient solution for detecting and monitoring open-pit coal mines. Incorporating artificial intelligence into dynamic coal mining activities yields valuable insights that aid in making informed decisions for sustainability. © 2023 ACRS. All Rights Reserved.

关键词： Artificial Intelligence Coal Mine Deep Learning Monitoring Multispectral SAR Sentinel Supervised Classification U-Net

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The Ocean Current Measurement Signal Processing Method Based on Squinted Dual-antenna Interferometric SAR 6

The Ocean Current Measurement Signal Processing Method Based...

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6th International Conference on Electronics technology, ICET 2023

作者： Yin, Jianfeng Zhang, Qingjun He, Zongbo Liu, Jie Wang, Aiming Yan, He Fang, Qingyuan Beijing Institute of Spacecraft System Engineering China Aerospace Science and Technology Corporation Beijing China Institute of Remote Sensing Satellite China Aerospace Science and Technology Corporation Beijing China Nanjing University of Aeronautics and Astronautics School of Electronic and Information Engineering Nanjing China University of Electronic Science and Technology of China School of Information & Communication Engineering Chengdu China

ISBN: (纸本)9798350337693

The signal processing method of ocean current measurement based on squinted dual-antenna interferometric SAR (InSAR) is studied in this paper. There is an along-track baseline component in the imaging geometry of squinted cross-track InSAR, which can be used for ocean current measurement and this mode can extend the application efficiency of DEM acquisition InSAR. In this context, the principle of ocean current velocity measurement method based on squinted cross-track InSAR is introduced. The signal processing method and procedure are studied. BP (Back Projection) algorithm is adopted in SAR imaging, and the flat ground interferometric phase expression is derived. The effectiveness of the proposed method is verified by echo simulation and data processing. © 2023 IEEE.

关键词： Electric current measurement

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From Laboratory to Field:Unsupervised Domain Adaptation for Plant Disease Recognition in the Wild

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Plant Phenomics 2023年第2期5卷 195-208页

作者： Xinlu Wu Xijian Fan Peng Luo Sruti Das Choudhury Tardi Tjahjadi Chunhua Hu College of Information Science and Technology Nanjing Forestry UniversityNanjing 210037China Institute of Forest Resource Information Techniques Chinese Academy of ForestryBeijing 100091China Key Laboratory of Forestry Remote Sensing and Information System National Forestry and Grassland AdministrationBeijing 100091China Department of Computer Science and Engineering University of Nebraska-LincolnLincolnNE 68588USA School of Engineering University of WarwickCoventry CV47ALUK

Plant disease recognition is of vital importance to monitor plant development and predicting crop ***,due to data degradation caused by different conditions of image acquisition,e.g.,laboratory *** environment,machine learning-based recognition models generated within a specific dataset(source domain)tend to lose their validity when generalized to a novel dataset(target domain).To this end,domain adaptation methods can be leveraged for the recognition by learning invariant representations across *** this paper,we aim at addressing the issues of domain shift existing in plant disease recognition and propose a novel unsupervised domain adaptation method via uncertainty regularization,namely,Multi-Representation Subdomain Adaptation Network with Uncertainty Regularization for Cross-Species Plant Disease Classification(MSUN).Our simple but effective MSUN makes a breakthrough in plant disease recognition in the wild by using a large amount of unlabeled data and via nonadversarial ***,MSUN comprises multirepresentation,subdomain adaptation modules and auxiliary uncertainty *** multirepresentation module enables MSUN to learn the overall structure of features and also focus on capturing more details by using the multiple representations of the source *** effectively alleviates the problem of large interdomain *** adaptation is used to capture discriminative properties by addressing the issue of higher interclass similarity and lower intraclass ***,the auxiliary uncertainty regularization effectively suppresses the uncertainty problem due to domain *** was experimentally validated to achieve optimal results on the PlantDoc,Plant-Pathology,Corn-Leaf-Diseases,and Tomato-Leaf-Diseases datasets,with accuracies of 56.06%,72.31%,96.78%,and 50.58%,respectively,surpassing other state-of-the-art domain adaptation techniques considerably.

关键词： Plant breakthrough details

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Groundwater Recharge Planning Using Field Survey for Talupula Mandal in Anantapur District, Andhra Pradesh, India

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Nature Environment and Pollution technology 2021年第5期20卷 1981-1987页

作者： Nagamani, K. Batvari, Prabhu Dass Packialakshmi, S. Reddy, C. Sai Kumar Anuradha, B. Center for Remote Sensing and Geoinformatics Sathyabama Institute of Science and Technology Chennai India Center for Earth and Atmospheric Science Sathyabama Institute of Science and Technology Chennai India Department of Civil Engineering Sathyabama Institute of Science and Technology Chennai India Chennai Institute of Technology Chennai India

Groundwater is essential to the sustainability of India’s environment, economy, and living conditions because it isn’t just the primary source of domestic supply of water in rural areas, but it is also the major and most productive origin of the water. The increased demand for groundwater as a result of reduced rainfall has put a strain on groundwater resources in areas where groundwater is the primary supply of water. The main aim of this study is to identify and explore the groundwater potential zones in Talupula Mandal of 280.3 km2 in Ananthapur district in Andhra Pradesh, India with semi-arid climatic conditions. Based on the field survey approach, groundwater availability is found out in the villages. Schlumberger Vertical Electrical Sounding (VES) survey technique was used to discover the resistivity and thickness of the unmistakable layers. It was carried out in 18 randomly selected sites where groundwater plays an important role in agricultural and domestic use. The thickness and resistivity of first- and second-layer crack sites of the various layers were separated from ground data using IPI2WIN programming. Using software, graphs were plotted and groundwater potential zones were identified for recharging the groundwater. Based on the results, different models of recharge structures for the study area are identified and recommended. Hence the management of groundwater paves the way for sustainable groundwater levels. © 2021 Technoscience Publications. All rights reserved.

关键词： Rural areas

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Effectiveness of Spatiotemporal Data Fusion in Fine-Scale Land Surface Phenology Monitoring:A Simulation Study

Journal of Remote Sensing

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Journal of remote sensing 2024年第1期4卷 632-647页

作者： Jiaqi Tian Xiaolin Zhu Miaogen Shen Jin Chen Ruyin Cao Yuean Qiu Yi Nam Xu Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic UniversityHong KongChina Department of Geography National University of SingaporeSingaporeSingapore State Key Laboratory of Earth Surface Processes and Resource Ecology Institute of Remote Sensing Science and EngineeringFaculty of Geographical ScienceBeijing Normal UniversityBeijing 100875China School of Resources and Environment University of Electronic Science and Technology of ChinaChengduSichuan 611731China Department of Geography Environmentand Spatial SciencesMichigan State UniversityEast LansingMI 48824USA

Spatiotemporal data fusion technologies have been widely used for land surface phenology(LSP)monitoring since it is a low-cost solution to obtain fine-resolution satellite time ***,the reliability of fused images is largely affected by land surface heterogeneity and input *** is unclear whether data fusion can really benefit LSP studies at fine *** explore this research question,this study designed a sophisticated simulation experiment to quantify effectiveness of 2 representative data fusion algorithms,namely,pair-based Spatial and Temporal Adaptive Reflectance Fusion Model(STARFM)and time series-based Spatiotemporal fusion method to Simultaneously generate Full-length normalized difference vegetation Index Time series(SSFIT)by fusing Landsat and Moderate Resolution Imaging Spectroradiometer(MODIS)data in extracting pixel-wise spring phenology(i.e.,the start of the growing season,SOS)and its spatial gradient and temporal *** results reveal that:(a)STARFM can improve the accuracy of pixel-wise SOS by up to 74.47%and temporal variation by up to 59.13%,respectively,compared with only using Landsat images,but it can hardly improve the retrieval of spatial *** SSFIT,the accuracy of pixel-wise SOS,spatial gradient,and temporal variation can be improved by up to 139.20%,26.36%,and 162.30%,respectively;(b)the accuracy improvement introduced by fusion algorithms decreases with the number of available Landsat images per year,and it has a large variation with the same number of available Landsat images,and(c)this large variation is highly related to the temporal distributions of available Landsat images,suggesting that fusion algorithms can improve SOS accuracy only when cloud-free Landsat images cannot capture key vegetation growth *** study calls for caution with the use of data fusion in LSP studies at fine scales.

关键词： data fusion land surface phenology sophisticated simulation experiment fused images satellite time series land surface land surface phenology lsp monitoring spatiotemporal data fusion

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Assessment of CH_(4) flux and its influencing drivers in the rice-wheat agroecosystem of the Huai River Basin,China

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Journal of Integrative Agriculture 2024年第12期23卷 4203-4215页

作者： Xiaolan Yu Fangmin Zhang Yanqiu Fang Xiaohan Zhao Kaidi Zhang Yanyu Lu Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Jiangsu Key Laboratory of Agricultural Meteorology College of Ecology and Applied MeteorologyNanjing University of Information Science&TechnologyNanjing 210044China Anhui Institute of Meteorological Sciences/Anhui Province Key Laboratory of Atmospheric Science and Satellite Remote Sensing Hefei 230031China Shouxian National Climatology Observatory Huai River Basin Typical Farm Eco-Meteorological Experiment Field of China Meteorological AdministrationShouxian 232200China

To understand the CH_(4) flux variations and their climatic drivers in the rice-wheat agroecosystem in the Huai River Basin of China,the CH_(4) flux was observed by using open-path eddy covariance at a typical rice-wheat rotation system in Anhui Province,China from November 2019 to October *** variations and their drivers were then analyzed with the Akaike information criterion ***_(4) flux showed distinct diurnal variations with single peaks during 9:00-13:00 local *** highest peak was 2.15μg m^(-2)s^(-1)which occurred at 11:00 in the vegetative growth stage in the rice growing season(RGS).CH_(4) flux also showed significant seasonal *** average CH_(4)flux in the vegetative growth stage in the RGS(193.8±74.2 mg m^(-2)d^(-1))was the highest among all growth *** annual total CH_(4) flux in the non-rice growing season(3.2 g m^(-2))was relatively small compared to that in the RGS(23.9 g m^(-2)).CH_(4) flux increased significantly with increase in air temperature,soil temperature,and soil water content in both the RGS and the non-RGS,while it decreased significantly with increase in vapor pressure deficit in the *** study provided a comprehensive understanding of the CH_(4) flux and its drivers in the rice-wheat rotation agroecosystem in the Huai River Basin of *** addition,our findings will be helpful for the validation and adjustment of the CH_(4) models in this region.

关键词： CH_(4) flux eddy covariance method rice-wheat rotation agroecosystem Huai River Basin

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Deriving Accurate Intertidal Topography for Sandy Beaches Using ICESat-2 Data and Sentinel-2 Imagery

Journal of Remote Sensing

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Journal of remote sensing 2024年第1期4卷 80-88页

作者： Nan Xu Lin Wang Hao Xu Yue Ma Yao Li Xiao Hua Wang College of Geography and Remote Sensing Hohai UniversityNanjingChina School of Earth Sciences and Engineering Hohai UniversityNanjingChina College of Geodesy and Geomatics Shandong University of Science and TechnologyQingdaoChina School of Electronic Information Wuhan UniversityWuhanChina School of Geographical Sciences Southwest UniversityChongqingChina Sino-Australian Research Consortium for Coastal Management School of ScienceUniversity of New South WalesCanberraNSWAustralia

Sandy beaches are one of the most dominant coastal types across the world,which can protect the coast and provide *** intertidal zone is inundated with dynamic tides,making it challenging to obtain the intertidal topography near sandy beaches due to their narrow widths,particularly in sparsely populated areas with rare in situ ***,the Ice,Cloud,and land Elevation Satellite 2(ICESat-2)photon-counting lidar offers unprecedented finer observations in elevation in coastal *** this study,we aimed to explore the capacity of combining ICESat-2 altimeter data and Sentinel-2 imagery in deriving the intertidal topography for sandy *** results indicate that the satellite-derived sandy beach intertidal topography(with a resolution of~3 m)in Texas,USA exhibits a good accuracy of 0.35 m compared with a local high-resolution digital elevation model ***,we generated the coastal lowland map from the beach topography and validated the lowland results using high-resolution *** highlighted that our proposed method for inverting the topography of sandy beaches are based on globally accessible satellite data(i.e.,without in situ data),demonstrating the potential for applications in various coastal zones around the world.

关键词： obtain intertidal topography Coastal zones ICESat sandy beaches protect coast Intertidal topography Satellite data Sentinel

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Integrating Sentinel-1 and 2 with LiDAR data to estimate aboveground biomass of subtropical forests in northeast Guangdong, China

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International Journal of Digital Earth 2023年第1期16卷 158-182页

作者： Linjing Zhang Xiaoxue Zhang Zhenfeng Shao Wenhao Jiang Huimin Gao College of Geodesy and Geomatics Shandong University of Science and TechnologyQingdaoPeople’s Republic of China Key Laboratory of Ocean Geomatics Ministry of Natural ResourcesQingdaoPeople’s Republic of China State key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanPeople’s Republic of China

Accurate monitoring of forest aboveground biomass(AGB)is vital for sustainable forest ***,the AGB is estimated by combining satellite images andfield ***,field plots are inaccessible in complex forest *** to the limitations related to either optical or SAR data alone,combining the two data types is ***,we explored the LiDAR-derived biomass as training/testing samples for the combination of yearly time-series of Sentinel-1 and Sentinel-2 images to estimate AGB in north-eastern Conghua,Guangdong province,*** AGB reference map derived from airborne LiDAR data andfield plots could provide more samples for satellite-based AGB estimation with a limited amount of sampling *** designed four groups of experiments based on diverse Sentinel-1 and Sentinel-2 variables,including backscatter,backscatter indices,texture features,spectral bands,vegetation indices,and biophysical *** different prediction methods(support vector regression,multi-layer perceptron neural network,K-nearest neighbour,and random forest(RF))were separately used to estimate *** showed that the RF model achieved the best accuracy for AGB *** Sentinel-1 and Sentinel-2 experiment(R^(2):0.72,RMSEr:17.65%)performed better than the monthly complementary experiment(R^(2):0.66,RMSEr:19.01%).Additionally,the arid period images were observed to be sensitive for estimating *** most contributing Sentinels predictors were determined via a sequential forward ***,the proposed methodology has great potential for low-cost,large-scale,and high-precision AGB estimation.

关键词： Sentinel liDAR aboveground biomass non-parametric models subtropical forest

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Bridge detection method for HSRRSIs based on YOLOv5 with a decoupled head

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International Journal of Digital Earth 2023年第1期16卷 113-129页

作者： Mulan Qiua Liang Huang Bo-Hui Tang Faculty of Land Resources Engineering Kunming University of Science and TechnologyKunmingPeople’s Republic of China Key Laboratory of Plateau Remote Sensing Yunnan Provincial Department of EducationKunmingPeople’s Republic of China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of SciencesBeijingPeople’s Republic of China

The different imaging conditions of high spatial resolution remote sensing images(HSRRSIs)tend to cause large differences in the background information of bridges from the images,including problems of difficult detection of multiscale bridges,leakage of small bridges and insufficient detection accuracy for their *** address these problems,a YOLOv5 network with a decoupled head for the automatic detection of bridges in HSRRIs is proposed in this ***,the problem of inconsistent scale of information fusion of each feature in the feature pyramid network is solved using a weighted bi-directional feature pyramid network(BiFPN).Then,the convolutional block attention module(CBAM)is fused into the three effective feature layers after feature pyramid network *** bridge feature information is effectively extracted from the channel and spatial ***,the decoupled head is fused in the YOLO Head to separate the classifier and regressor to speed up the network convergence and improve the network detection accuracy ***,the practical effect is evaluated by calculating the average precision(AP).According to the experimental results,the AP of the proposed method is 98.1%,which is improved by 4.1%∼23.5%compared with other models.

关键词： HSRRIs bridge detection BiFPN CBAM feature fusion decoupled head

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