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Research on the Application of the Radiative Transfer Model Based on Deep Neural Network in One-dimensional Variational Algorithm

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Journal of Tropical Meteorology 2022年第3期28卷 326-342页

作者： HE Qiu-rui ZHANG Rui-ling LI Jiao-yang WANG Zhen-zhan School of Information Technology Luoyang Normal UniversityLuoyangHenan 471934 China Key Laboratory of Microwave Remote Sensing National Space Science CenterChinese Academy of SciencesBeijing 100190 China Department of Electrical and Computer Engineering Michigan State UniversityEast LansingMI 48824 USA

As a typical physical retrieval algorithm for retrieving atmospheric parameters,one-dimensional variational(1 DVAR)algorithm is widely used in various climate and meteorological communities and enjoys an important position in the field of microwave remote *** algorithm parameters affecting the performance of the 1 DVAR algorithm,the accuracy of the microwave radiative transfer model for calculating the simulated brightness temperature is the fundamental constraint on the retrieval accuracies of the 1 DVAR algorithm for retrieving atmospheric *** this study,a deep neural network(DNN)is used to describe the nonlinear relationship between atmospheric parameters and satellite-based microwave radiometer observations,and a DNN-based radiative transfer model is developed and applied to the 1 DVAR algorithm to carry out retrieval experiments of the atmospheric temperature and humidity *** retrieval results of the temperature and humidity profiles from the Microwave Humidity and Temperature Sounder(MWHTS)onboard the Feng-Yun-3(FY-3)satellite show that the DNN-based radiative transfer model can obtain higher accuracy for simulating MWHTS observations than that of the operational radiative transfer model RTTOV,and also enables the 1 DVAR algorithm to obtain higher retrieval accuracies of the temperature and humidity *** this study,the DNN-based radiative transfer model applied to the 1 DVAR algorithm can fundamentally improve the retrieval accuracies of atmospheric parameters,which may provide important reference for various applied studies in atmospheric sciences.

关键词： one-dimensional variational algorithm radiative transfer model deep neural network FY-3 MWHTS temperature and humidity profiles

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Multi-objective Pedestrian Tracking Based on Attention Multi-Scale Feature Fusion with Dynamic Sample

Multi-objective Pedestrian Tracking Based on Attention Multi...

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Electronic Communication and Artificial Intelligence (IWECAI), International Workshop on

作者： Yulong Zhang Huafeng Chen School of Information Engineering Huzhou University Huzhou China School of Information Science and Technology Zhejiang Shuren University Hangzhou China Remote Sensing Image Processing and Application International Science and Technology Cooperation Base of Zhejiang Province mHangzhou China

ISBN: (数字)9798350386943

ISBN: (纸本)9798350386950

We proposed a detection framework that utilizes dynamic upsampling operators inattention-scale sequence fusion and applied to multi-object pedestrian tracking tasks. For small objects, we employed the Channel and Spatial Attention Combiner (CSAC) module to combines the Multi-scale Feature Fusion (MSFF) module and the Multiple Feature Concatenation Encoder (MFCE) module to enhance the information extraction capability of the network. Additionally, in the feature extraction stage, we use lightweight and efficient dynamic upsampling operators. Experiments on two mainstream multi-object pedestrian tracking datasets (MOT17, MOT20) demonstrate that combining various mainstream trackers with the proposed model improves the accuracy of multi-object pedestrian tracking.

关键词： Pedestrians Accuracy Object detection Feature extraction Information retrieval Artificial intelligence

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Vector transformation and reduction to the pole for regional magnetic data with variable field direction

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Applied Geophysics 2022年第1期19卷 107-116,145,146页

作者： Luo Yao Wu Mei-Ping China Aero Geophysical Survey and Remote Sensing Center for Natural and Resources Beijing 100083China College of Intelligence Science and Technology National University of Defense TechnologyChangsha 410073China.

The vector transformation and pole reduction from the total-field anomaly are signifi cant for the *** examined these industry-standard processing procedures in the Fourier *** propose a novel iteration algorithm for regional magnetic anomalies transformations to derive the vertical-component data from the total-field measurements with the variation in the core-fi eld direction over the ***,we use the same algorithm to convert the calculated vertical-component data into the corresponding data at the pole and realize the processing of diff erential reduction to the pole(DRTP).Unlike Arkani-Hamed’s DRTP method,the two types of iterative algorithms have the same forms,and DRTP is realized by implementing this algorithm *** synthetic model’s calculation results show that the method has high accuracy,and the fi eld data processing confi rms its practicality.

关键词： magnetic anomaly diff erential reduction to the pole three-component magnetic data large-area magnetic survey

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Evapotranspiration estimation using SEBAL algorithm integrated with remote sensing and experimental methods

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International Journal of Digital Earth 2021年第11期14卷 1638-1658页

作者： Nazila Shamloo Mohammad Taghi Sattari Halit Apaydin Khalil Valizadeh Kamran Ramendra Prasad Department of Water Engineering Agriculture FacultyUniversity of TabrizTabriz ***Iran Department of Agricultural Engineering Faculty of AgricultureAnkara UniversityAnkara 06110 Turkey Department of Remote Sensing and GIS University of TabrizTabriz ***Iran Department of Science School of Science and TechnologyThe University of FijiFiji

Evapotranspiration is one of the most important elements of the hydrological *** of evapotranspiration is imperative for effective forest,irrigation,rangeland and water resources management as well as to increase yields and for better crop *** study aims to evaluate the effectiveness of the Surface Energy Balance Algorithm for Land(SEBAL)in estimating evapotranspiration and crop coefficient of corn in the Mediterranean region of Adana province,*** Landsat 8 satellite images from March to September 2018 were used to acquire the coefficients of the respective ***,the net radiation flux on the earth’s surface and the earth’s heat flux is obtained using incoming-outgoing radiation fluxes from albedo,surface emissivity coefficients,land surface temperature,and plant ***,the sensible heat flux is calculated by determining the hot and cold pixels under consideration via the atmospheric stability ***,evapotranspiration maps are *** crop coefficient of corn is also estimated with the respected maps being *** validate the outcomes from the SEBAL algorithm,experimental methods were employed to calculate the evapotranspiration values and evaluated using suitable performance *** results showed that the SEBAL generated evapotranspiration values are in high agreement with the FAO Penman-Monteith method registering the highest correlation(R=0.91)and the lowest error(RMSE=1.14).In addition,the SEBAL method registered the highest correlation values of 0.89,0.87 and 0.68 with Turk,Makkink and Hargreaves experimental methods,***,the crop coefficients estimated using SEBAL also manifested an acceptable correlation with all *** highest correlation value registered was with the FAO Penman-Monteith method(R=0.98).The outcomes show that since the performance of the SEBAL algorithm in estimating the actual evapotranspiration and crop coefficient using Landsat 8 satellite images

关键词： Actual evapotranspiration crop coefficient corn SEBAL Landsat 8 Adana

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Ship Detection in SAR Images Using Attentive YOLOv8 Architecture

Ship Detection in SAR Images Using Attentive YOLOv8 Architec...

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remote sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Sushma R Iliger Damodar Reddy Edla Subrat Kumar Acharya P Manjusree GOA Department of Computer Science and Engineering National Institute of Technology GOA GOA INDIA National Remote Sensing Centre ISRO Advanced Analytics Division HYDERABAD INDIA

ISBN: (数字)9798350390346

ISBN: (纸本)9798350390353

Use of remote sensing technology in recent years has seen a rapid growth especially by government organisations to identify objects in marine ecosystems in remote sensed images. Ship detection is one such task that has seen extensive experimentation inorder to build efficient techniques to produce reliable and accurate detection results. Ship targets in Synthetic Aperture Radar (SAR) images suffer from significant scattering, a complicated background, and ambiguous contour information. The goal of our work presented in this paper is to use the most recent iteration of the You only look once (YOLO) model to detect both inshore and offshore ships in SAR images with the introduction of attention mechanism. We utilised two datasets namely HRSID and our own custom datasets to train different version of YOLOv8, YOLOv9 and Yolov10 models along with our proposed model. The experimental outcomes showcase advancements, with the introduction of the Shuffle Attention mechanism resulting in a remarkable 6.72% increase in precision and a 3.58% boost in ${\mathrm {mAP}} \text{@}$ 0.5: 0.9 evaluation metrics when validated on custom dataset. Our study contributes to the refinement of lightweight baseline YOLO model, demonstrating the efficacy of incorporating shuffle attention mechanisms and a methodology to produce ship detection results on large resolution SAR images in smallest inference time with limited computing resources.

关键词： YOLO Attention mechanisms Accuracy Computational modeling Sentinel-1 Computer architecture Radar polarimetry Marine vehicles remote sensing Synthetic aperture radar

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On the effects of spatial resolution on effective distance measurement in digital landscapes

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Ecological Processes 2021年第1期10卷 659-677页

作者： Rachel Mundeli Murekatete Takeshi Shirabe KTH Royal Institute of Technology School of Architecture and the Built EnvironmentTeknikringen 10A11428 StockholmSweden University of Rwanda College of Science and TechnologyCentre for Geographic Information Systems and Remote SensingKN 67 Street NyarugengeKigaliRwanda

Background:Connectivity is an important landscape attribute in ecological studies and conservation practices and is often expressed in terms of effective *** the cost of movement of an organism over a landscape is effectively represented by a raster surface,effective distances can be equated with the cost-weighted distance of least-cost *** is generally recognized that this measure is sensitive to the grid’s cell size,but little is known if it is always sensitive in the same way and to the same degree and if not,what makes it more(or less)*** conducted computational experiments with both synthetic and real landscape data,in which we generated and analyzed large samples of effective distances measured on cost surfaces of varying cell sizes derived from those *** particular focus was on the statistical behavior of the ratio—referred to as‘accuracy indicator’—of the effective distance measured on a lower-resolution cost surface to that measured on a higher-resolution cost ***:In the experiment with synthetic cost surfaces,the sample values of the accuracy indicator were generally clustered around 1,but slightly greater with the absence of linear sequences(or barriers)of high-cost or inadmissible cells and smaller with the presence of such *** latter tendency was more dominant,and both tendencies became more pronounced as the difference between the spatial resolutions of the associated cost surfaces *** two real satellite images(of different resolutions with fairly large discrepancies)were used as the basis of cost estimation,the variation of the accuracy indicator was found to be substantially large in the vicinity(1500 m)of the source but decreases quickly with an increase in distance from ***:Effective distances measured on lower-resolution cost surfaces are generally highly correlated with—and useful predictors of—effective distances measured on higher-resolution cost *** relationship t

关键词： Landscape connectivity Effective distance Raster-based least-cost path model Accuracy Spatial resolution

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Evaluation of the Terrain Elevation Estimates over Forested Areas From Spaceborne Full-Waveform Lidar Missions: GLAS and GEDI

Evaluation of the Terrain Elevation Estimates over Forested ...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Hailan Jiang Yi Li Weihua Li Anxin Ding Guangjian Yan Xihan Mu Donghui Xie Feng Yang Kaijian Xu Ping Zhao Jun Geng Felix Morsdorf School of Resources and Environmental Engineering Hefei University of Technology Hefei China State Key Laboratory of Remote Sensing Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing China State Key Laboratory of Remote Sensing Science Faculty of Geographical Science Beijing Normal University Beijing China Beijing Engineering Research Center for Global Land Remote Sensing Products Faculty of Geographical Science Beijing Normal University Beijing China College of Soil and Water Conservation Science and Engineering Northwest A&F University Xian China School of Civil Engineering Hefei University of Technology Hefei China Department of Geography University of Zurich Zurich Switzerland

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Terrain elevation over forested areas is important for studies such as hydrological modeling and soil erosion. The spaceborne full-waveform LiDAR missions including Geoscience Laser Altimeter System (GLAS) and Global Ecosystem Dynamics Investigation (GEDI) provide freely available terrain elevation products indirectly and directly. However, the accuracies have seldom been evaluated in the same region. Here, we examined the terrain elevation accuracy and assessed the influence of terrain slope in forested areas using high-resolution airborne LiDAR data as a reference. The root mean square error (RMSE) of terrain elevation computed from all the data of GLAS and GEDI is 5.1 m and 8.4 m, respectively. Even though the footprint diameter of GEDI is much smaller than GLAS (25 m vs. 65 m), we still found a significant terrain effect with the increase of slope in GEDI. The RMSE of terrain elevation from GLAS is 3.4 m, 7.6 m, and 10.5 m when the slope ranges from 0° to 30° with an increment of 10°. The RMSE of terrain elevation from GEDI is 5.2 m, 8.8 m, 12.2 m, 14.1 m, and 16. 9 m when the slope ranges from 0° to 50° with an increment of 10°.

关键词： Laser radar Accuracy Satellites Ecosystems Forestry Soil Laser modes

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A Seasonal-Series Lstm Network for Irregular Urban Function Zone Recognition Using Sentinel-2 Images

SSRN

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SSRN 2024年

作者： Hu, Ting Han, Mengyu Guo, Zixuan School of Remote Sensing and Geomatics Engineering Nanjing University of Information Science and Technology Nanjing210044 China Technology Innovation Center for Integration Application in Remote Sensing and Navigation Ministry of Natural Resources Nanjing210044 China Jiangsu Engineering Center for Collaborative Navigation/ Positioning and Smart Applications Nanjing210044 China

Urban Functional Zone (UFZ) is the fundamental unit for urban planning and management, which plays a crucial role in improving urban management efficiency and optimizing urban layout. The classification of UFZs has benefited from the rich information and fine features provided by high spatial resolution (HSR) remote sensing images. The seasonal variations of ground objects differ across different UFZs. However, HSR remote sensing images typically lack seasonal information and come with high acquisition costs. Therefore, this study proposes a classification framework for UFZ based on all-seasonal Sentinel-2 remote sensing images, learning the spectral-spatial-temporal features for detailed mapping of UFZs. Firstly, to balance the significant scale difference of block (i.e., the fundamental mapping unit) size in 10-meter resolution remote sensing images, an adaptive gradient perception (AGP) mechanism is used to guide the feature extraction of different-scale blocks. Secondly, the bag of visual words (BOVW) model is employed to extract block-level spectral-spatial features. Thirdly, a seasonal series LSTM network is proposed to capture block-level temporal dynamic information, particularly focusing on the spectral-temporal patterns of different UFZs. The proposed framework is applied to UFZ classification in four typical cities in China. The overall accuracy (OA) for all cases reaches around 93%, which is an improvement of approximately 7% compared to other methods. Our results demonstrate the superiority and portability of this framework, as well as the significant potential of open-source remote sensing images in large-scale UFZ mapping. © 2024, The Authors. All rights reserved.

关键词： remote sensing

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Deep Neural Network Based Automatic Grounding Line Delineation In Dinsar Interferograms

Deep Neural Network Based Automatic Grounding Line Delineati...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Sindhu Ramanath Tarekere Lukas Krieger Konrad Heidler Dana Floricioiu Remote Sensing Technology Institute German Aerospace Center Oberpfaffenhofen Germany Data Science in Earth Observation Technical University of Munich Germany

Accurate identification of grounding lines is of immense importance for estimating the mass budgets of ocean-terminating ice sheets and glaciers of Antarctica and Greenland. In Differential Interferometric SAR (DInSAR) interferograms, human experts still largely manually digitize grounding lines. The time-consuming nature of this task makes it infeasible to produce timely, continent-wide grounding line mappings. This study employed a Deep Neural Network (DNN) to automate delineation. The Holistically-Nested Edge Detection (HED) network was trained in a supervised manner on features derived from interferometric phase, elevation data, ice velocity, tidal amplitude, atmospheric pressure and corresponding manual delineations. HED-generated lines achieved a median deviation of 209 m with a median absolute deviation of 153 m from manual delineations. The developed automatic pipeline demonstrates the potential for generating spatially and temporally dense mappings of the grounding line.

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PyramidMamba: Rethinking Pyramid Feature Fusion with Selective Space State Model for Semantic Segmentation of remote sensing Imagery

arXiv

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arXiv 2024年

作者： Wang, Libo Li, Dongxu Dong, Sijun Meng, Xiaoliang Zhang, Xiaokang Hong, Danfeng School of Remote Sensing and Geomatics Engineering Nanjing University of Information Science and Technology Jiangsu Nanjing210044 China Technology Innovation Center for Integration Applications in Remote Sensing and Navigation Ministry of Natural Resources Jiangsu Nanjing210044 China School of Remote Sensing and Information Engineering Wuhan University Wuhan430079 China The School of Information Science and Engineering Wuhan University of Science and Technology Wuhan430081 China The Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China The School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

Semantic segmentation, as a basic tool for intelligent interpretation of remote sensing images, plays a vital role in many Earth Observation (EO) applications. Nowadays, accurate semantic segmentation of remote sensing images remains a challenge due to the complex spatial-temporal scenes and multi-scale geo-objects. Driven by the wave of deep learning (DL), CNN- and Transformer-based semantic segmentation methods have been explored widely, and these two architectures both revealed the importance of multi-scale feature representation for strengthening semantic information of geo-objects. However, the actual multi-scale feature fusion often comes with the semantic redundancy issue due to homogeneous semantic contents in pyramid features. To handle this issue, we propose a novel Mamba-based segmentation network, namely PyramidMamba. Specifically, we design a plug-and-play decoder, which develops a dense spatial pyramid pooling (DSPP) to encode rich multi-scale semantic features and a pyramid fusion Mamba (PFM) to reduce semantic redundancy in multi-scale feature fusion. Comprehensive ablation experiments illustrate the effectiveness and superiority of the proposed method in enhancing multi-scale feature representation as well as the great potential for real-time semantic segmentation. Moreover, our PyramidMamba yields state-of-the-art performance on three publicly available datasets, i.e. the OpenEarthMap (70.8% mIoU), ISPRS Vaihingen (84.8% mIoU) and Potsdam (88.0% mIoU) datasets. The code will be available at https://***/WangLibo1995/GeoSeg. Copyright © 2024, The Authors. All rights reserved.

关键词： Redundancy

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