IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing最新文献

Volcano-Seismic Event Detection and Clustering 火山地震事件检测与聚类

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2025-04-28 DOI: 10.1109/JSTARS.2025.3559412

Joe Carthy;Pablo Rey-Devesa;Manuel Titos;Carmen Benitez

引用次数: 0

Enhancing the Accuracy of Jason-3 PWV Products Over Coastal Areas Using the Back Propagation Neural Network 利用反向传播神经网络提高Jason-3型PWV产品在沿海地区的精度

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2025-04-16 DOI: 10.1109/JSTARS.2025.3559732

Yangzhao Gong;Zhizhao Liu

{"title":"Enhancing the Accuracy of Jason-3 PWV Products Over Coastal Areas Using the Back Propagation Neural Network","authors":"Yangzhao Gong;Zhizhao Liu","doi":"10.1109/JSTARS.2025.3559732","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559732","url":null,"abstract":"The performance of microwave radiometers aboard altimetric satellites in measuring water vapor degrades significantly over coastal areas due to the mixing of land within its footprint. In this study, we propose using the back propagation neural network (BPNN) models to enhance the accuracy of Jason-3 precipitable water vapor (PWV) over coastal areas. PWV data from 2076 globally distributed coastal and island Global Navigation Satellite System (GNSS) stations and 237 radiosonde stations are used as the reference. Specifically, the GNSS PWV data in 2016 and 2017 are used to train the BPNN models, while the GNSS and radiosonde PWV observations from January 2018 to June 2023 are used to test the performances of the BPNN models proposed. Our results show that the proposed BPNN PWV models can considerably enhance the accuracy of Jason-3 PWV recorded in the coastal areas (within 25 km of land). Evaluated by the GNSS PWV, BPNN models can reduce the root mean square error (RMSE) of Jason-3 PWV in the coastal areas from 4.2 to 2.7 kg/m2 (35.7% of RMSE reduction). Assessed by the radiosonde PWV, the results indicate that the RMSE of Jason-3 PWV in the coastal areas is decreased from 5.0 to 3.6 kg/m2 (28.0% of RMSE reduction) after using the proposed BPNN models.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10684-10693"},"PeriodicalIF":4.7,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10967237","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896406","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

Mapping Antarctic Blue Ice Areas With Sentinel-2A/B Images and LightGBM Model 利用Sentinel-2A/B图像和LightGBM模型绘制南极蓝冰区域

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2025-04-15 DOI: 10.1109/JSTARS.2025.3560280

Xiaolong Teng;Jiahui Xu;Xiangbin Cui;Guitao Shi;Zhengyi Hu;Qingyu Gu;Bailang Yu;Jianping Wu;Yan Huang

{"title":"Mapping Antarctic Blue Ice Areas With Sentinel-2A/B Images and LightGBM Model","authors":"Xiaolong Teng;Jiahui Xu;Xiangbin Cui;Guitao Shi;Zhengyi Hu;Qingyu Gu;Bailang Yu;Jianping Wu;Yan Huang","doi":"10.1109/JSTARS.2025.3560280","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3560280","url":null,"abstract":"Antarctic blue ice plays a crucial role in surface energy balance and paleoclimate research. A high-accuracy and comprehensive dataset of blue ice areas (BIAs) is essential for understanding climate dynamics and environmental changes in the region. While satellite remote sensing is effective in mapping BIAs, traditional methods rely on limited spectral bands and linear models with inherent limitations. This study integrated remote sensing techniques with ensemble learning algorithms to develop a high-resolution (10 m) Antarctic-wide BIA dataset using Sentinel-2 imagery based on the years 2017–2022. Random forest, XGBoost, and LightGBM integrated learning algorithms were used to model the extraction of Antarctic blue ice. The accuracy of the model was evaluated by confusion matrix with LightGBM achieving the highest overall accuracy (87.23%). We also used SHapley Additive exPlanations values to improve the interpretability of opaque system models by evaluating the contribution of each feature variable. Validation through visual interpretation of Sentinel-2A/B images further confirmed the model's reliability, with an accuracy of 90.61%. Based on these robust results, we generated detailed BIAs across Antarctica. Our findings estimate the total BIAs at 175 274 km2, covering approximately 1.25% of the continent. The blue ice is mainly concentrated in low-elevation coastal areas and mountain slopes, especially in Dronning Maud Land, Amery Ice Shelf, Wilkes Land, Victoria Land, and Transantarctic Mountains. We further reveal that most of the blue ice is located at elevations below 500 m, with air temperatures between −5 and 0 °C, and ice velocity under 100 m/yr. Our high-resolution dataset provides crucial insights for future research in Antarctic glaciology, paleoclimate studies, and meteorite collection.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"11078-11092"},"PeriodicalIF":4.7,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10964008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900498","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

Mapping County-Level Rice Planting Areas by Joint Use of High-Resolution Optical and Time Series SAR Imagery 高分辨率光学影像与时间序列SAR影像联合用于县级水稻种植区制图

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2025-04-14 DOI: 10.1109/JSTARS.2025.3560992

Jia Xu;Haojie Wang;Lin Qiu;Hui Wang;Yang Mu

{"title":"Mapping County-Level Rice Planting Areas by Joint Use of High-Resolution Optical and Time Series SAR Imagery","authors":"Jia Xu;Haojie Wang;Lin Qiu;Hui Wang;Yang Mu","doi":"10.1109/JSTARS.2025.3560992","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3560992","url":null,"abstract":"Timely and accurate mapping of rice spatial distribution is needed for ensuring food security, managing water usage, and optimizing agricultural production. Frequent cloudy and rainy weather during the rice growing season presents challenges in constructing comprehensive time-series features from optical images. In addition, the fragmentation and sparsity of farmland parcels within the county lead to low extraction accuracy. To address the above challenges, this study proposed an automated rice mapping framework for county-level rice mapping in cloudy and rainy regions by integrating the strengths of high-resolution optical and time-series Synthetic Aperture Radar (SAR) imagery. First, the HRTSNet model was developed to extract farmland parcels from GF-6 high resolution imagery. Subsequently, the long short-term memory (LSTM)-based temporal classification model was utilized to acquire rice cultivation information at parcel scale using time-series Sentinel-1 SAR data. The proposed method was validated at two counties in China. The results showed that the HRTSNet model achieved the highest mIoU and delineated the closest boundary maps to ground truth in extracting farmland parcels from high-resolution optical imagery. And the proposed method effectively integrated limited GF-6 imagery with time-series Sentinel-1 data and performed better than traditional machine learning algorithms like Random Forest and pixel-based methods, achieving an overall accuracy of over 88% and a Kappa coefficient of over 86% for the Dangtu and Rudong counties. In addition, the classification accuracy was effectively improved by incorporating the DPSVIm index. The results provide a potential solution for mapping county-level rice planting areas with limited optical imagery.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10547-10561"},"PeriodicalIF":4.7,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10964810","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900504","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

Planetary Boundary Layer Height Estimation: Methodology and Case Study Using NAST-I FIREX-AQ Field Campaign Data 行星边界层高度估计：使用NAST-I FIREX-AQ野外战役数据的方法和案例研究

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2025-04-11 DOI: 10.1109/JSTARS.2025.3556546

Hyun-Sung Jang;Daniel K. Zhou;Xu Liu;Wan Wu;Allen M. Larar;Anna M. Noe

{"title":"Planetary Boundary Layer Height Estimation: Methodology and Case Study Using NAST-I FIREX-AQ Field Campaign Data","authors":"Hyun-Sung Jang;Daniel K. Zhou;Xu Liu;Wan Wu;Allen M. Larar;Anna M. Noe","doi":"10.1109/JSTARS.2025.3556546","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3556546","url":null,"abstract":"The ratio of potential temperature (Tp) and dewpoint temperature (Td), which is derived from retrievals of infrared hyperspectral measurements, is adopted as a new parameter for better estimating planetary boundary layer height (PBLH). A case study, conducted with National Airborne Sounder Testbed-Interferometer (NAST-I) measurements obtained during the Fire Influence on Regional to Global Environments and Air Quality field campaign, is presented herein. We use NAST-I geophysical parameter retrievals from the Single Field-of-view Sounder Atmospheric Product algorithm, which ensures higher vertical resolution of temperature and moisture profiles as well as accurate surface temperature and emissivity, to estimate PBLH with a higher horizontal spatial resolution of 2.6 km. As a result of using the ratio of potential and dewpoint temperatures, instead of individual thermodynamic retrievals, a more robust parameter for estimating PBLH is obtained. A quality control process is developed to filter out abnormal outliers. Additionally, those outliers are modified using statistics from nominal distributions of the Tp/Td ratio and PBLH. A high consistency between NAST-I thermodynamically-retrieved PBLH and that from the European Centre for Medium-Range Weather Forecasts Reanalysis-5, which uses both dynamic and thermodynamic information, successfully supports the validity and significance of our approach.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10002-10009"},"PeriodicalIF":4.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10962543","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850847","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

Cross Validation of FY3D MWRI Passive Microwave LST With MODIS LST Under Clear-Sky Conditions 晴空条件下FY3D MWRI无源微波地表温度与MODIS地表温度的交叉验证

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2025-04-11 DOI: 10.1109/JSTARS.2025.3560132

Yuting Gong;Huifang Li;Xiuqing Hu;Huanfeng Shen

{"title":"Cross Validation of FY3D MWRI Passive Microwave LST With MODIS LST Under Clear-Sky Conditions","authors":"Yuting Gong;Huifang Li;Xiuqing Hu;Huanfeng Shen","doi":"10.1109/JSTARS.2025.3560132","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3560132","url":null,"abstract":"Passive microwave (PMW) land surface temperature (LST) is widely used in climatology, agriculture, and environmental science due to its large range, all-weather capabilities, and other unique advantages. FY3D microwave radiation imager (MWRI) offers some of the global rare PMW LST products, but the accuracy is not clear due to the lack of research on the validation of the FY3D MWRI LST products. Therefore, cross validation of FY3D MWRI LST with moderate-resolution imaging spectroradiometer LST was investigated in this study. Based on the quality control, temporal matching, and spatial matching, the spatial and temporal distribution were validated between the two LST products from 2019 to 2023. In the spatial cross validation, the overall correlation coefficient (<italic>R) of the two daily LST products distributed between 0.68 and 0.84, and the deviation ranged from 3 to 8 K. The <italic>R of the two LST monthly products was between 0.78 and 0.89, with the deviation mainly distributed between 3 and 6 K. This suggests that the monthly product is more stable and has a more reliable accuracy than the daily FY3D MWRI LST product. In the temporal cross validation, the seven dominant global land cover types were analyzed. It can be found that the main distribution of <italic>R was in the range of 0.7–0.9, and the deviation was mainly 3–6 K. With the improvement of the FY3D MWRI LST official retrieval algorithm, the accuracy of the FY3D MWRI LST products has improved significantly, and the products deserve extensive attention.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10786-10802"},"PeriodicalIF":4.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10963739","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896519","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

RSEFormer: A Residual Squeeze-Excitation-Based Transformer for Pixelwise Hyperspectral Image Classification RSEFormer：一种基于残差挤压激励的高光谱图像分类变压器

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2025-04-11 DOI: 10.1109/JSTARS.2025.3559190

Yusen Liu;Hao Zhang;Fashuai Li;Fei Han;Yicheng Wang;Hao Pan;Boyu Liu;Guoliang Tang;Genghua Huang;Tingting He;Yuwei Chen

{"title":"RSEFormer: A Residual Squeeze-Excitation-Based Transformer for Pixelwise Hyperspectral Image Classification","authors":"Yusen Liu;Hao Zhang;Fashuai Li;Fei Han;Yicheng Wang;Hao Pan;Boyu Liu;Guoliang Tang;Genghua Huang;Tingting He;Yuwei Chen","doi":"10.1109/JSTARS.2025.3559190","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559190","url":null,"abstract":"Hyperspectral image (HSI) classification plays an essential role in remote sensing image processing. Deep learning methods, especially the transformer, has achieved great success in HSI classification. However, due to the limited existing labeled data of HSI, the relation between objects is irregular in such a small dataset. Merely using the long-range attention based on transformers for learning may lead to bias results. In addition, it is challenging for current attention-based methods to extract attention between high-dimensional spectra, which affects the performance of the classification model. To this end, we propose a network that combines local spectral attention and global spatial-spectral attention, the residual depthwise separable squeeze-and-extraction transformer for HSI classification. Our framework integrates 3-D depthwise separable convolution (DSC) squeeze-and–excitation module, residual block, and sharpened attention vision transformer (SA-ViT) to extract spatial-spectral features from HSI. Three-dimensional DSC squeeze-and–excitation extracts spatial-spectral features and learns the local spectral implicit attention. Residual connection is introduced to hamper gradient vanishment during the network training. For global modeling, SA-ViT employs diagonal masking to eliminate self-token bias and learnable temperature parameters to sharpen attention score. Experimental results demonstrate that our method outperforms other approaches on five HSI benchmark datasets, achieving state-of-the-art performance.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"11308-11323"},"PeriodicalIF":4.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10962545","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900505","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

Infrared Small Target Detection via Multidirectional Local Gravitational Force and Level-Line Connectivity 基于多方向局部引力和水平线连通性的红外小目标检测

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2025-04-11 DOI: 10.1109/JSTARS.2025.3560306

Xuying Hao;Xianyuan Liu;Yujia Liu;Yijuan Qiu;Yunjing Zhang;Yi Cui;Tao Lei

{"title":"Infrared Small Target Detection via Multidirectional Local Gravitational Force and Level-Line Connectivity","authors":"Xuying Hao;Xianyuan Liu;Yujia Liu;Yijuan Qiu;Yunjing Zhang;Yi Cui;Tao Lei","doi":"10.1109/JSTARS.2025.3560306","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3560306","url":null,"abstract":"Infrared small target detection is significantly challenged by residual high-intensity background edges and a low signal-to-noise ratio. These issues hinder accurate target differentiation from the background and heighten the risk of false alarms. To address these challenges, we propose a method that employs multidirectional local gravitational force (LGF) contrast combined with level-line connectivity (LLC) contrast. The LGF model integrates information from each pixel within the local region and introduces a new sigmoid function to reduce noise, enabling fine-grained gradient detection. The magnitude and orientation in this gradient can then be used to differentiate the target from the background. Considering that the target exhibits different gradient features in different directions, we further propose a multidirectional LGF contrast. This contrast utilizes the distribution characteristics of LGF magnitude to enhance the target and effectively suppress strong edges. In addition, to fully utilize the orientation information in the LGF, we designed the LLC contrast based on the spatial consistency of the target, increasing the difference between the target and the background. Finally, we propose a regional fusion technique to weight the two contrasts, improving background suppression while preserving target intensity. Experimental results demonstrate the effectiveness of our method in detecting targets within high-intensity edge backgrounds, complex textures, and noisy environments. Compared to other state-of-the-art methods, our method significantly improves detection accuracy.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"11111-11127"},"PeriodicalIF":4.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10964054","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900532","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

Nighttime PM2.5 Concentration Estimation Based on NPP-VIIRS and Interpretable Machine Learning: The Case of Beijing–Tianjin–Hebei Region 基于NPP-VIIRS和可解释机器学习的夜间PM2.5浓度估算——以京津冀地区为例

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2025-04-11 DOI: 10.1109/JSTARS.2025.3560136

Tong Li;Bo Li;Zhihua Han;Wenhao Zhang;Xiufeng Yang

{"title":"Nighttime PM2.5 Concentration Estimation Based on NPP-VIIRS and Interpretable Machine Learning: The Case of Beijing–Tianjin–Hebei Region","authors":"Tong Li;Bo Li;Zhihua Han;Wenhao Zhang;Xiufeng Yang","doi":"10.1109/JSTARS.2025.3560136","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3560136","url":null,"abstract":"Air pollution and public health issues caused by fine particulate matter (PM2.5) are becoming increasingly severe. Although well-established satellite methods exist for retrieving daytime PM2.5 concentrations, these methods are limited by weak nighttime light radiation. To resolve these challenges, this study proposed a nighttime PM2.5 concentration estimation method based on explainable machine learning and low-light data. Owing to the complexity of nighttime light sources, primarily composed of artificial lighting and moonlight, both types of light were considered by simulating lunar irradiance and artificial light radiance. This study utilized nighttime lighting, meteorological, various geospatial auxiliary, simulated nighttime light radiation, and ground-based PM2.5 monitoring data to construct a dataset with an effective sample size of 24,311. A deep neural network model was trained to estimate nighttime PM2.5 concentrations. The experimental results show that, after adding the simulated nighttime light radiation, the tenfold cross-validation <italic>R2 of the model improved from 0.6 to 0.73. In addition, 74% of site-based tenfold cross-validation <italic>R2 values exceeded 0.7, indicating the model's robust spatial adaptability. The model was then used to estimate nighttime PM2.5 concentrations in the study area for 2021. The Shapley additive explanation model was applied to analyze the effect curves of different predictors on nighttime PM2.5 to examine the contributions of various factors. This study can serve as a reference for similar research in the future, and the proposed retrieval method offers a broad coverage of nighttime PM2.5 data, providing a useful supplement to ground station measurements.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"11047-11059"},"PeriodicalIF":4.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10963726","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896285","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

Sequential Inversion for Helicopter Time-Domain Electromagnetics Based on a Regularized Extended Kalman Filtering 基于正则化扩展卡尔曼滤波的直升机时域电磁序列反演

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2025-04-10 DOI: 10.1109/JSTARS.2025.3559504

Sirui Zhou;Jun Lin;Chuandong Jiang;Haigen Zhou;Yanzhang Wang

{"title":"Sequential Inversion for Helicopter Time-Domain Electromagnetics Based on a Regularized Extended Kalman Filtering","authors":"Sirui Zhou;Jun Lin;Chuandong Jiang;Haigen Zhou;Yanzhang Wang","doi":"10.1109/JSTARS.2025.3559504","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559504","url":null,"abstract":"The helicopter time-domain electromagnetic method (HTEM) has been widely applied in challenging geological terrains, particularly in large-scale mineral exploration, underground water resource detection, and the selection of sites for underground engineering, due to its advantage of not requiring personnel to enter the detection area. Currently, the 1-D inversion method with lateral constraints, which is commonly used in HTEM, faces challenges quickly delivering inversion results in the field due to its high computational demands and lengthy processing time. In this article, we propose a sequential inversion method for HTEM based on the regularized extended Kalman filter (REKF). The REKF algorithm is used to predict the current inversion result at a given time by using the inversion result from the previous moment, and predictions are corrected with the observed data at that specific time. We also introduce a vertical roughness regularization term to avoid overfitting issues during the inversion process. Based on the sequential processing strategy of measuring while inverting, the REKF algorithm yields the optimal solution of the inversion objective function in just a few iterations, or even a single iteration, enabling near real-time calculations. In the simulation experiments, the advantages of the REKF method are demonstrated by comparing the inversion results of the REKF method with those of the extended Kalman filter method and the Occam method with lateral constraints. Finally, we perform REKF inversion on HTEM data obtained from a location in Xinjiang, China. The results demonstrate the accuracy and practicality of the REKF inversion method.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10896-10908"},"PeriodicalIF":4.7,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10960538","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896520","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