IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing最新文献_第9页

Complex Cloud-Sea Background Simulation for Space-Based Infrared Payload Digital Twin 天基红外载荷数字孪生体复杂云海背景仿真

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-12-27 DOI: 10.1109/JSTARS.2024.3523395

Wen Sun;Yejin Li;Fenghong Li;Guangsen Liu;Peng Rao

{"title":"Complex Cloud-Sea Background Simulation for Space-Based Infrared Payload Digital Twin","authors":"Wen Sun;Yejin Li;Fenghong Li;Guangsen Liu;Peng Rao","doi":"10.1109/JSTARS.2024.3523395","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3523395","url":null,"abstract":"The advent of Industry 4.0 has highlighted the requirements for the digitization and intelligent evolution of space-based payloads. To address challenges like limited data samples and simulate infrared images in various scenarios, this study proposes a hybrid data-driven and fractal-driven cloud-sea scenario simulation approach for high-precision infrared images at space-based detection scales. Static cloud-sea scenes are generated using Qilu-2 and New Technology satellite images, while dynamic scenarios are simulated with our iterative fractal dimension optimization algorithm. Next, we propose a high-precision infrared cloud-sea simulation method based on these simulate scenarios. Finally, we validate the confidence of the simulated images through morphological assessment using a 2-D histogram and radiative accuracy evaluation based on Moderate resolution atmospheric transmission (MODTRAN) results. Experimental results confirm the method's accuracy, showing close alignment with on-orbit images. In the 2.7–3.0 μm band, our average radiance is consistent with MODTRAN. Specifically, for reflection angles below 60<inline-formula><tex-math>$^circ$</tex-math></inline-formula>, the root mean square error between our results and MODTRAN results is about 12.3% in the 3.0–5.0 μm band, and around 3.7% in the 8.0–14.0 μm band. Morphological assessment shows an average error of about 8.3% when compared to on-orbit images. This method allows for generating multiband, multispecies, and multiscale complex cloud-sea scenario images for digital infrared payloads with high flexibility and confidence.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"3025-3042"},"PeriodicalIF":4.7,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10817102","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Electrical Conductivity of Mantle Transition Zone and Water Content Revealed by the Magnetic Data of China Seismo-Electromagnetic Satellite 中国地震电磁卫星磁资料揭示的地幔过渡带电导率和含水量

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-12-27 DOI: 10.1109/JSTARS.2024.3523671

Mingquan Lai;Xiuyan Ren;Changchun Yin;Yunhe Liu;Xinpeng Ma;Yinglin Wang;Shufan Zhao

{"title":"Electrical Conductivity of Mantle Transition Zone and Water Content Revealed by the Magnetic Data of China Seismo-Electromagnetic Satellite","authors":"Mingquan Lai;Xiuyan Ren;Changchun Yin;Yunhe Liu;Xinpeng Ma;Yinglin Wang;Shufan Zhao","doi":"10.1109/JSTARS.2024.3523671","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3523671","url":null,"abstract":"The mantle transition zone (MTZ) plays a key role in the deep global material cycle, while the water content in MTZ is debated from saturated to dry. Since the electrical conductivity is highly sensitive to water, its accurate estimation will greatly help reveal the water content. The high quality and plenty of data are crucial for global-scale conductivity recovery. In this article, we use the magnetic vector data of China seismo-electromagnetic satellite (CSES) to estimate the global mantle electrical structure, accompanying with the Swarm satellite and observatories. In particular, we correct the latitude effect of CSES Level 2 data. The radial conductivity model and uncertainty information of the Earth are obtained by using Bayesian inversion. It is found that large changes in the electrical results of MTZ occur when using the CSES magnetic field data. The conductivity is higher than that inverted from Swarm data, but lower than that from the observatory data. Finally, we, respectively, invert the resistivity structure of the MTZ with two years and nearly nine years of database of CSES, Swarm, and observatories, and analyze the laboratory conductivity model. The results indicate that the water content of the MTZ is less than 0.01 weight%.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"3173-3184"},"PeriodicalIF":4.7,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10817569","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RGB-T Object Detection With Failure Scenarios RGB-T对象检测失败场景

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-12-27 DOI: 10.1109/JSTARS.2024.3523408

Qingwang Wang;Yuxuan Sun;Yongke Chi;Tao Shen

{"title":"RGB-T Object Detection With Failure Scenarios","authors":"Qingwang Wang;Yuxuan Sun;Yongke Chi;Tao Shen","doi":"10.1109/JSTARS.2024.3523408","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3523408","url":null,"abstract":"Currently, RGB-thermal (RGB-T) object detection algorithms have demonstrated excellent performance, but issues such as modality failure caused by fog, strong light, sensor damage, and other conditions can significantly impact the detector's performance. This article proposes a multimodal object detection method named diffusion enhanced object detection network (DENet), aiming to address modality failure problems caused by nonroutine environments, sensor anomalies, and other factors, while suppressing redundant information in multimodal data to improve model accuracy. First, we design a multidimensional incremental information generation module based on a diffusion model, which reconstructs the unstable information of RGB-T images through the reverse diffusion process using the original fusion feature map. To further address the issue of redundant information in existing RGB-T object detection models, a redundant information suppression module is introduced, minimizing cross-modal redundant information based on mutual information and contrastive loss. Finally, a kernel similarity-aware illumination module (KSIM) is introduced to dynamically adjust the weighting of RGB and thermal features by incorporating both illumination intensity and the similarity between modalities. KSIM can fine-tune the contribution of each modality during fusion, ensuring a more precise balance that improves recognition performance across diverse conditions. Experimental results on the DroneVehicle and VEDAI datasets show that DENet performs outstandingly in multimodal object detection tasks, effectively improving detection accuracy and reducing the impact of modality failure on performance.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"3000-3010"},"PeriodicalIF":4.7,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10817087","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mixed Analytical-Numerical Modeling of Radar Backscattering for Seasonal Snowpacks

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-12-27 DOI: 10.1109/JSTARS.2024.3521612

Martina Lodigiani;Carlo Marin;Marco Pasian

{"title":"Mixed Analytical-Numerical Modeling of Radar Backscattering for Seasonal Snowpacks","authors":"Martina Lodigiani;Carlo Marin;Marco Pasian","doi":"10.1109/JSTARS.2024.3521612","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3521612","url":null,"abstract":"The intensity of the backscattered signal collected by active radars over wet, seasonal snowpacks depends on numerous variables related to the snowpack, which are often difficult to determine accurately. In recent years, thanks to the increased availability of spaceborne synthetic aperture radars (SARs), a temporal relationship between wet-snow metamorphism and microwave backscattering has been demonstrated. However, a precise quantitative description of this phenomenon has yet to be fully determined. In this article, we propose a new mixed analytical-numerical model to describe the effect of the physical parameters related to the wet snowpack metamorphism on the intensity of the backscattering at L, C, and X bands, with a focus on high alpine snowpacks. Particular attention was paid to integrate the effects of the snow superficial roughness and the snow scattering. The model is first applied to several simulated snowpacks and then validated against a real multitemporal SAR signature acquired by Sentinel-1 over the snow station of Malga Fadner (South Tyrol, Italy) and of Torgnon (Aosta Valley, Italy). The comparison between the model outcomes and the satellite data were in good agreement, leading to the possibility of using such method for operational identification of the run-off phase from remote locations.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"3461-3471"},"PeriodicalIF":4.7,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10817100","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hierarchical Recognition for Urban Villages Fusing Multiview Feature Information 融合多视角特征信息的城中村层次识别

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-12-26 DOI: 10.1109/JSTARS.2024.3522662

Zhenkang Wang;Nan Xia;Song Hua;Jiale Liang;Xiankai Ji;Ziyu Wang;Jiechen Wang

{"title":"Hierarchical Recognition for Urban Villages Fusing Multiview Feature Information","authors":"Zhenkang Wang;Nan Xia;Song Hua;Jiale Liang;Xiankai Ji;Ziyu Wang;Jiechen Wang","doi":"10.1109/JSTARS.2024.3522662","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3522662","url":null,"abstract":"Urban village (UV) renovation is crucial for urban renewal, with effective UV recognition serving as a prerequisite. While existing studies on UV recognition predominantly rely on high-resolution remote sensing images (RSI), and few integrate street view images (SVI), which could cause confusion in regions with similar planar features, such as old residential area and industrial parks. This article proposed a hierarchical framework for UV recognition which integrated multiview images. The spectral, textural, and structural features were extracted from Google RSI by machine-learning classifiers for each segmented block. The deep-learning method was applied to SVI to capture the architectural feature at each viewpoint. The rule-constrained fusion was conducted to combine the block-level and point-level UV recognition results. Taking a typical high-density megacity Nanjing as the study area, a high recognition overall accuracy (OA) and Kappa of 95.04% and 0.860 were achieved, identifying 172 UVs covering an area of 27.93 km2 by 2020. The results demonstrated an “urban village ring” pattern in the city, with central urban areas showing a “multicenter and multicluster” spatial distribution, while suburban areas exhibited “large and concentrated” characteristics. Compared with results from single-view of RSI, the complementarity with SVI for multiview features increased the OA and Kappa by 3.34% and 0.079, which could effectively distinguish the old industrial parks. We believe that our proposed hierarchical framework is essential to the scientific and accurate UV recognition, which can guide the urban management and high-quality development.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"3344-3355"},"PeriodicalIF":4.7,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10816389","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multimodel Combination Bathymetry Inversion Approach Based on Geomorphic Segmentation in Coral Reef Habitats Using ICESat-2 and Multispectral Satellite Images 基于ICESat-2和多光谱卫星影像的珊瑚礁地貌分割多模式组合测深反演方法

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-12-26 DOI: 10.1109/JSTARS.2024.3523296

Xiuling Zuo;Juncan Teng;Fenzhen Su;Zhengxian Duan;Kefu Yu

{"title":"Multimodel Combination Bathymetry Inversion Approach Based on Geomorphic Segmentation in Coral Reef Habitats Using ICESat-2 and Multispectral Satellite Images","authors":"Xiuling Zuo;Juncan Teng;Fenzhen Su;Zhengxian Duan;Kefu Yu","doi":"10.1109/JSTARS.2024.3523296","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3523296","url":null,"abstract":"Owing to the high spatial heterogeneity of substrate types and terrain, the present satellite-derived bathymetry (SDB) methods have low accuracy in deriving large-scale bathymetry in coral reef habitats. Taking 11 coral reefs of Xisha Islands (ocean area of 607 km2) in the South China Sea as the study area, a parametric multimodel combination approach based on geomorphic segmentation (PMCGS) for obtaining bathymetry was constructed by combining the Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) data with Gaofen-1 (GF-1) medium- and Worldview-2/3 (WV-2/3) high-resolution multispectral images. In this approach, five parametric SDB models were trained in each geomorphic zone by combining ICESat-2 and multispectral satellite images. Then, the optimal SDB models of each geomorphic zone were combined and extrapolated to other coral reefs in the same geomorphic zone. Results showed that the multiple ratios model was optimal for the reef flat, shallow lagoon, and patch reef zones. The binomial model was optimal for the reef slope and deep lagoon zones. Validated by the in situ bathymetric data and ICESat-2 data, the bathymetry inverted using the PMCGS had an RMSE of 0.91 m in GF-1 image and 0.70–0.88 m in WV-2/3 images when extrapolated to other reefs, which is significantly more accurate than active–passive one entire model methods with the same resolution. Our method performed better at 0–10 m and 15–25 m depth than the results obtained from previous studies, especially in the shallow water areas of the reef flat and shallow lagoon. The proposed PMCGS can efficiently improve the bathymetry inversion accuracy of medium- and high-resolution satellite images and it has great potential applications in deriving large-scale bathymetry, especially in Indo-Pacific coral reef habitats.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"3267-3280"},"PeriodicalIF":4.7,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10816658","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessment of Satellite-Derived FAPAR Products With Different Spatial Resolutions for Gross Primary Productivity Estimation 不同空间分辨率下卫星衍生FAPAR产品对总初级生产力估算的评估

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-12-26 DOI: 10.1109/JSTARS.2024.3522938

Xiyu Zhang;Huaan Jin;Wei Zhao;Gaofei Yin;Xinyao Xie;Jianrong Fan

{"title":"Assessment of Satellite-Derived FAPAR Products With Different Spatial Resolutions for Gross Primary Productivity Estimation","authors":"Xiyu Zhang;Huaan Jin;Wei Zhao;Gaofei Yin;Xinyao Xie;Jianrong Fan","doi":"10.1109/JSTARS.2024.3522938","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3522938","url":null,"abstract":"Accurate estimation of gross primary productivity (GPP) is crucial for understanding terrestrial carbon cycles and assessing ecosystem health. Light use efficiency (LUE) models, which are widely used for the generation of regional or global GPP products, often rely on the fraction of absorbed photosynthetically active radiation (FAPAR). However, most of the existing FAPAR products with moderate to coarse spatial resolutions introduce uncertainties in GPP estimations across heterogeneous landscapes. In this work, the MODerate resolution Imaging Spectroradiometer (MODIS) FAPAR product at the 500-m resolution, along with a new HIgh-spatial-resolution Global LAnd Surface Satellite (Hi-GLASS) FAPAR dataset at the 30-m resolution, was used to drive an LUE model for GPP estimations at 188 eddy covariance (EC) sites. Then, they were compared and evaluated based on the EC GPP measurements. Results showed that Hi-GLASS FAPAR provided the GPP estimates with more detailed spatial information compared with MODIS FAPAR. Moreover, Hi-GLASS FAPAR significantly improved GPP estimations, with an overall <italic>R2 increase from 0.54 (MODIS) to 0.63 (Hi-GLASS) and a root-mean-square error (RMSE) decrease from 3.04 to 2.70 gC⋅m−2⋅day−1. In addition, 75% of the selected sites exhibited enhanced <italic>R2 values with Hi-GLASS FAPAR, demonstrating its application potential in GPP estimations across different vegetation types. Specifically, crop sites exhibited the most notable improvements, with an <italic>R2 increase of 0.16 and an RMSE decrease of 0.70 gC⋅m−2⋅day−1. These findings highlight the advantages of high-resolution FAPAR data in capturing spatial heterogeneity and improving the accuracy of GPP estimations and underscore its potential for refined ecosystem monitoring.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"3087-3098"},"PeriodicalIF":4.7,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10816383","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Radar Jamming Recognition: Models, Methods, and Prospects 雷达干扰识别：模型、方法与展望

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-12-26 DOI: 10.1109/JSTARS.2024.3522951

Zan Wang;Zhengwei Guo;Gaofeng Shu;Ning Li

{"title":"Radar Jamming Recognition: Models, Methods, and Prospects","authors":"Zan Wang;Zhengwei Guo;Gaofeng Shu;Ning Li","doi":"10.1109/JSTARS.2024.3522951","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3522951","url":null,"abstract":"In modern warfare with complex and changeable electromagnetic environments, radar jamming is getting more complex and realistic, which poses a serious threat to radar; jamming recognition has become a hot topic in the field of electronic countermeasures. To make effective antijamming measures, numerous jamming recognition methods have been proposed. This article presents a systematic review of jamming recognition for this topic. Specifically, first building a system framework for jamming models, including deception jamming, suppression jamming, and smart jamming, thoroughly explaining the operational mechanisms. Then, recognition methods based on traditional machine learning are summarized and are delved into the advantages and disadvantages of feature extraction methods and classifiers. Furthermore, the focus shifts to neural network-based methods, such as shallow neural network methods and deep neural network methods. In particular, restricted sample strategies are also discussed as potential future directions. Finally, conclusions on the current status of jamming recognition methods and the prospects for future work are made. This article provides a reference for the research of radar jamming recognition.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"3315-3343"},"PeriodicalIF":4.7,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10816388","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparison of Tropospheric Horizontal Gradients Modeling Methods in LEO Constellation Augmented GNSS Precise Point Positioning LEO星座增强GNSS精确点定位对流层水平梯度建模方法比较

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-12-26 DOI: 10.1109/JSTARS.2024.3523023

Xinyu Zhang;Wenwu Ding;Xiaochuan Qu;Hongjin Xu;Xuanzhao Tan;Yunbin Yuan

{"title":"Comparison of Tropospheric Horizontal Gradients Modeling Methods in LEO Constellation Augmented GNSS Precise Point Positioning","authors":"Xinyu Zhang;Wenwu Ding;Xiaochuan Qu;Hongjin Xu;Xuanzhao Tan;Yunbin Yuan","doi":"10.1109/JSTARS.2024.3523023","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3523023","url":null,"abstract":"With the improvement in GNSS data processing accuracies, the selection of optimal asymmetric troposphere delay modeling method becomes essential, especially during the period of severe weather events and with the development of low Earth orbit (LEO) constellation augmented GNSS (LeGNSS). In this research, we compare the performances of several troposphere gradient models in describing the asymmetrical troposphere delays. Using simulation data during the stable and severe periods, we find that the high-order horizontal gradient models exhibit higher accuracy in the experiments. In the LeGNSS precision point positioning solutions, the second-order gradient model performs optimally, with accuracies of up to 1.1/3.8/0.8 mm during the stable period and 0.9/2.5/1.0 mm during the severe period for the horizontal component, vertical component, and zenith total delay (ZTD) parameters. In comparison, the analysis of slant path delays accuracy for elevation below 10° shows that the directional model is more suitable for low elevation observations, but the introduction of too many redundant parameters leads to a decrease in the accuracy at high elevation angles. The LEO constellation can bring maximum 32.9%, 12.6%, and 27.9% accuracy improvement for the horizontal component, vertical component, and ZTD parameters during the stable period, while 26.5%, 31.8%, and 34.9% during the severe period. The estimation of high-temporal-resolution gradient parameters instead of traditional daily gradient parameters can significantly improve the accuracy of ZTD in the extreme weather events. Therefore, this research underscores the spatial and temporal resolution of horizontal gradient models, which meets the growing demand for GNSS/LeGNSS data processing during the severe weather events.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"3011-3024"},"PeriodicalIF":4.7,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10816382","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multiscale Feature-Enhanced Water Body Detector of Truncated Gaussian Clutter in SAR Imagery SAR图像截断高斯杂波的多尺度特征增强水体检测器

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-12-26 DOI: 10.1109/JSTARS.2024.3522997

Bo Zhu;Yuli Xia;Yongsheng Zhou;Xiaoning Lv;Minqin Liu

{"title":"Multiscale Feature-Enhanced Water Body Detector of Truncated Gaussian Clutter in SAR Imagery","authors":"Bo Zhu;Yuli Xia;Yongsheng Zhou;Xiaoning Lv;Minqin Liu","doi":"10.1109/JSTARS.2024.3522997","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3522997","url":null,"abstract":"This article presents a multiscale feature-enhanced water detector using truncated Gaussian clutter (TGCFeWD) for synthetic aperture radar (SAR) imagery. It aims to improve detection accuracy through adaptive elimination of high-intensity outliers and shadows. High-intensity outliers cause overestimation of parameters used for water data statistical modeling, resulting in inaccurate thresholds. Furthermore, shadows in SAR imagery have similar digital numbers and statistical parameters to water, making them difficult to distinguish. To address these two key issues, this article proposes a comprehensive approach comprised of three main components: Part A is to calculate Gaussian distance based on which the land surfaces or artificial objects can be removed easily by Otsu. Part B is to expand the difference between water and shadows through feature enhancing. Part C is to segment water and shadows according to the expanding differences. The proposed TGCFeWD method effectively detects water bodies including seas, rivers, and lakes. Compared to several existing methods, TGCFeWD greatly improves water detection accuracy in complex environments. Based on metrics of accuracy, <italic>F1, and mean of intersection over union, TGCFeWD achieves the best performance (92.4%, 82.4%, and 80.1% for all data with five water body types) compared to several traditional methods, and even outperforms some neural-network-based methods in certain scenarios. The results are validated on the HISEA flooding dataset.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"3253-3266"},"PeriodicalIF":4.7,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10816489","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0