International journal of applied earth observation and geoinformation : ITC journal最新文献

{"title":"Large-area urban TomoSAR method with limited a priori knowledge and a complex deep learning model","authors":"Haoxuan Duan ,&nbsp;Yuzhou Liu ,&nbsp;Hong Zhang ,&nbsp;Peifeng Ma ,&nbsp;Zhongqi Shi ,&nbsp;Zihuan Guo ,&nbsp;Yixian Tang ,&nbsp;Fan Wu ,&nbsp;Chao Wang","doi":"10.1016/j.jag.2025.104521","DOIUrl":"10.1016/j.jag.2025.104521","url":null,"abstract":"<div><div>Buildings are crucial to cities, and tomographic synthetic aperture radar (TomoSAR) is an important tool for monitoring the heights, linear deformations and thermal amplitudes of buildings. However, existing TomoSAR height inversion methods do not fully leverage a priori knowledge, compromising the accuracy of deformation estimation; deep learning-based methods involve the integration of multiple steps, complicating the process. Additionally, the computational inefficiency of existing algorithms significantly hinders the large-scale practical deployment of TomoSAR. To address the above issues, this study proposes a novel large-area urban TomoSAR method integrating limited a priori knowledge constraints with a complex-valued (CV) deep learning model. By refining scatterer types and Permanent Scatterer (PS) height sample sets under limited a priori height data constraints, the proposed CV-TomoPS-Net establishes an end-to-end framework for scatterer classification and PS height regression. Additionally, the proposed fast beamforming method, paired with an adaptive spatial search mechanism, enables rapid large-area inversion of deformation and thermal amplitude parameters. Experiments were conducted in Shenzhen city using COSMO-SkyMed SAR data from 2020 to 2023 and limited a priori data. Results show that the proposed method improves the accuracy of scatterer type classification by 16 %, reduces the height calculation error by 30 %, and improves the monitoring efficiency by 80 % compared with the traditional beamforming method. Validation via corner reflectors deformation monitoring confirmed reliability, with a 1.5 mm average error. These results highlight the practical applicability of the proposed method for large-scale urban monitoring and its potential to provide technical support for sustainable urban development.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104521"},"PeriodicalIF":7.6,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BANet: A bilateral attention network for extracting changed buildings between remote sensing imagery and cadastral maps","authors":"Qingyu Li ,&nbsp;Lichao Mou ,&nbsp;Yilei Shi ,&nbsp;Xiao Xiang Zhu","doi":"10.1016/j.jag.2025.104486","DOIUrl":"10.1016/j.jag.2025.104486","url":null,"abstract":"<div><div>Up-to-date cadastral maps are vital to local governments in administrating real estate in cities. With its growing availability, remote sensing imagery is the cost-effective data for updating semantic contents on cadastral maps. In this study, we address the problem of updating buildings on cadastral maps, as city renewal is mainly characterized by new construction and demolition. While previous works focus on extracting all buildings from remote sensing images, we argue that these methods not only disregard preliminary information on cadastral maps but also fail to preserve building priors in unchanged areas on cadastral maps. Therefore, we focus on the task of extracting changed buildings (i.e., newly built and demolished buildings) from remote sensing images and cadastral maps. To address this task, we create an image-map building change detection (IMBCD) dataset, formed by around 27K pairs of remote sensing images and maps and their corresponding changed buildings in six distinct geographical areas across the globe. Accordingly, we propose a Bilateral Attention Network (BANet), introducing a novel attention mechanism: changed-first (CF) attention and non-changed-first (NCF) attention. This bilateral attention mechanism helps to refine the uncertain areas between changed and non-changed regions. Extensive experiments on our IMBCD dataset showcase the superior performance of BANet. Specifically, our BANet outperforms state-of-the-art models with F1 scores of 90.00% and 63.00% for the IMBCD-WHU and IMBCD-Inria datasets. This confirms that the leverage of bilateral attention blocks (BAB) can boost performance.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104486"},"PeriodicalIF":7.6,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seamless global daily soil moisture mapping using deep learning based spatiotemporal fusion","authors":"Menghui Jiang ,&nbsp;Tian Qiu ,&nbsp;Ting Wang ,&nbsp;Chao Zeng ,&nbsp;Boxuan Zhang ,&nbsp;Huanfeng Shen","doi":"10.1016/j.jag.2025.104517","DOIUrl":"10.1016/j.jag.2025.104517","url":null,"abstract":"<div><div>Soil moisture products with long-term, high spatial continuity, and high accuracy are essential for meteorological management and hydrological monitoring. Microwave remote sensing retrieval and land surface model simulation are the two primary sources of global-scale soil moisture data, but each has inherent limitations, making it difficult to balance accuracy and spatial coverage. In this paper, to tackle this challenge, we propose a deep learning-based spatiotemporal fusion framework to integrate the two data sources and generate a global soil moisture product with high spatial continuity and accuracy. Specifically, we leverage the high accuracy of the Soil Moisture Active and Passive (SMAP) microwave soil moisture data and the spatiotemporal continuity of the Noah assimilation soil moisture data. The proposed model employs a deep residual cycle GAN (DrcGAN) to capture the nonlinear complementary spatiotemporal features between the SMAP and Noah data, generating a seamless global daily product at a 36-km resolution, spanning April 4, 2015, to November 26, 2023, referred to as STSG-SM. Various validation methods, including spatial pattern analysis, time-series comparison, and in-situ validation, are utilized to assess the effectiveness and reliability of the product. In comparison to the selected in-situ measurements, the STSG-SM dataset (original SMAP-P₃₆) exhibits a bias of 0.0230 m³/m³ (0.0243 m³/m³), R of 0.8388 (0.8405), RMSE of 0.0629 m³/m³ (0.0628 m³/m³), and ubRMSE of 0.0585 m³/m³ (0.0579 m³/m³), indicating that the proposed method sustains the high precision of satellite-retrieved soil moisture and demonstrates strong consistency with the in-situ measurements.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104517"},"PeriodicalIF":7.6,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating and mapping tailings properties of the largest iron cluster in China for resource potential and reuse: A new perspective from interpretable CNN model and proposed spectral index based on hyperspectral satellite imagery","authors":"Haimei Lei ,&nbsp;Nisha Bao ,&nbsp;Moli Yu ,&nbsp;Yue Cao","doi":"10.1016/j.jag.2025.104512","DOIUrl":"10.1016/j.jag.2025.104512","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Iron tailings are crystalline powders predominantly composed of iron (Fe) and silicon dioxide (SiO&lt;sub&gt;2&lt;/sub&gt;). Spatially characterizing the physical and chemical properties of iron tailings is greatly important for optimal utilization and proper disposal of tailings. Visible-near infrared-shortwave infrared (VIS-NIR-SWIR; 350–2500 nm) spectroscopy offers a rapid, non-destructive, and cost-effective method for quantitatively analyzing tailings properties. This study aimed to quantify and map the spatial distribution of total Fe (TFe) and SiO&lt;sub&gt;2&lt;/sub&gt; contents of tailings dams at the largest iron cluster in China using laboratory spectra and GF-5 hyperspectral images. A total of 230 samples were collected from the surface of 11 tailings dams and scanned by a VIS–NIR–SWIR reflectance spectrometer in the laboratory. A novel spectral index was developed through a multi-objective programming methodology. This novel index utilizes band ratios to identify the optimal combination of spectral bands that show a strong correlation with concentrations of TFe and SiO&lt;sub&gt;2&lt;/sub&gt;. Simultaneously, it minimizes the impact of moisture content and particle size variations in surface tailings. In addition, the partial least squares regression (PLSR), random forest (RF) and convolutional neural network (CNN) algorithms based on laboratory spectra were used to calibrate spectral information with associated tailing properties. The contribution of wavelength in the calibration modeling process by calculating SHaply Additive exPlanations (SHAP) values. According to the results, the reflectance spectra were negatively correlated to TFe content and positively correlated to SiO&lt;sub&gt;2&lt;/sub&gt; content. The three-band spectral index (TBI) calculated by R&lt;sub&gt;827&lt;/sub&gt;/(R&lt;sub&gt;900&lt;/sub&gt; × R&lt;sub&gt;2200&lt;/sub&gt;) correlated best to TFe with the correlation coefficient (r) of 0.87, while the R&lt;sub&gt;2397&lt;/sub&gt;/(R&lt;sub&gt;776&lt;/sub&gt;×R&lt;sub&gt;900&lt;/sub&gt;) correlated best to SiO&lt;sub&gt;2&lt;/sub&gt; with r of 0.70. It also minimized the effect of particle size and moisture content on the reflectance spectra of tailings properties. The CNN algorithm with laboratory spectra yielded the highest estimation accuracy for TFe (R&lt;sup&gt;2&lt;/sup&gt; = 0.74, RPD = 1.79, RMSE = 3.69 %, LCCC = 0.74 and bias = -0.41) and SiO&lt;sub&gt;2&lt;/sub&gt; (R&lt;sup&gt;2&lt;/sup&gt; = 0.81, RPD = 2.15, RMSE = 1.28 %, LCCC = 0.86 and bias = −0.49). The direct standardization (DS) algorithm was applied to correct GF-5 hyperspectral image. Subsequently, the ability of TBI and the CNN model was compared for estimating and mapping the spatial distribution of TFe and SiO&lt;sub&gt;2&lt;/sub&gt; contents based on the corrected GF-5 images. The SHAP could obtain the wavelength contribution of the CNN model in tailings spectral modeling. It can be concluded that the proposed TBI is able to rapidly characterize the spatial distribution of tailings properties, and the interpretable CNN model can provide a technical mean for accurate estimation of tailings properties base","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104512"},"PeriodicalIF":7.6,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"WHU-RuR+: A benchmark dataset for global high-resolution rural road extraction","authors":"Ningjing Wang ,&nbsp;Xinyu Wang ,&nbsp;Yang Pan ,&nbsp;Wanqiang Yao ,&nbsp;Yanfei Zhong","doi":"10.1016/j.jag.2025.104518","DOIUrl":"10.1016/j.jag.2025.104518","url":null,"abstract":"<div><div>Efficient and accurate extraction of road networks from high-resolution satellite images is essential for urban planning, construction, and traffic management. Recently, various road datasets and advances in deep learning models have greatly enhanced road extraction techniques. However, challenges remain when trying to apply existing research to rural areas. Specifically, most public road datasets focus on urban areas and only contain a small number of rural scenes with complex backgrounds. The application of current public datasets for rural road extraction is challenging due to significant stylistic differences between urban and rural roads. In this article, a large-scale high-resolution remote sensing road dataset, termed WHU-RuR+, is proposed for rural road extraction, which contains 36,098 pairs of 1024 × 1024 high-resolution satellite images with the corresponding road annotation, covering a 6866.35 km² of rural areas in eight countries around the world. In addition, the article comprehensively summarizes the characteristics of this dataset and comprehensively evaluates advanced deep learning methods for road extraction on the WHU-RuR + dataset. Experimental results show that this dataset not only meets the application needs of rural road mapping but also has great practical application potential. At the same time, this article analyzes the challenges faced by rural road extraction and explores future research directions. The proposed WHU-RuR + rural road dataset will be available at the following URL: <span><span>http://rsidea.whu.edu.cn/WHU_RuR+_dataset.htm</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104518"},"PeriodicalIF":7.6,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143767773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"S2-IFNet: A spatial-semantic information fusion network integrated with boundary feature enhancement for forest land extraction from Sentinel-2 data","authors":"Junyang Xie ,&nbsp;Mengyao Zhang ,&nbsp;Hao Wu ,&nbsp;Anqi Lin ,&nbsp;Marcos Adami ,&nbsp;Abdul Rashid Mohamed Shariff ,&nbsp;Yahui Guo","doi":"10.1016/j.jag.2025.104505","DOIUrl":"10.1016/j.jag.2025.104505","url":null,"abstract":"<div><div>Accurately extracting forest land and understanding its spatial distribution are crucial for forest monitoring and management. However, variations in tree species, human activities, and natural disturbances create diverse and distinct forest land characteristics in remote sensing images, posing challenges for precise forest land extraction. To address these challenges, we propose a spatial-semantic information fusion network (S2-IFNet) integrated with boundary feature enhancement for forest land extraction from Sentinel-2 data. S2-IFNet employs a dual-branch network to separately extract the spatial and semantic features of forest land. In the semantic branch, two modules are introduced: a boundary enhancement module using the Sobel operator to capture forest land boundary details, and an attention module to strengthen the feature representation capability. Finally, a spatial-semantic fusion module effectively combines the spatial, semantic, and boundary detail information to improve the forest land extraction accuracy. S2-IFNet was evaluated across five regions in different global climate zones, with a comparative analysis conducted against four forest land-cover products in Yunnan, China. The results show that S2-IFNet can achieve an overall accuracy exceeding 90%, demonstrating its strong forest land extraction capability. Compared to the different forest land extraction models, S2-IFNet shows a superior performance, with ablation experiments confirming the effectiveness of each module. In particular, the boundary feature enhanced spatial-semantic fusion strategy enables S2-IFNet to focus more precisely on the boundaries and range of forest land, thereby enhancing the extraction accuracy. Meanwhile, S2-IFNet can adapt to complex scenarios, including varying forest density and similar object confusion. Furthermore, S2-IFNet can achieve results that are superior to the four other products.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104505"},"PeriodicalIF":7.6,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Occlusion mapping reveals the impact of flight and sensing parameters on vertical forest structure exploration with cost-effective UAV based laser scanning","authors":"Matthias Gassilloud,&nbsp;Barbara Koch,&nbsp;Anna Göritz","doi":"10.1016/j.jag.2025.104493","DOIUrl":"10.1016/j.jag.2025.104493","url":null,"abstract":"<div><div>Recent studies have demonstrated the potential of light detection and ranging (LiDAR) from uncrewed aerial vehicles (UAVs) for assessing forest structures. Maximizing data completeness and representativeness is essential to accurately retrieve key structural parameters. However, knowledge on how data acquisition approaches affect canopy volume exploration is sparse. This study investigated the effects of selected sensing and flight settings on canopy occlusion in a central European forest using a cost-effective sensor system. We conducted 44 flights with a DJI Matrice 300 RTK UAV and a DJI Zenmuse L1 LiDAR sensor, with different combinations of flight speed, azimuthal flight directions, sensor tilt angles, and scan patterns. Using sensor position reconstruction and a ray tracing algorithm to quantify occlusion, we found that: (1) A larger sensor tilt angle up to 15° increased total occlusion, enhancing exploration in the upper canopy, while decreasing it below due to reduced canopy penetration. (2) Flying multiple azimuthal directions with a linear scanning mode reduced vertical occlusion by up to 15.0% due to improved coverage from diverse perspectives. (3) Lissajous scanning patterns resulted in 10.1% less vertical occlusion compared to linear patterns, underscoring the importance of additional viewing angles. Based on these findings, we recommend: (a) incorporating nadir sampling for below-canopy assessment; (b) using off-nadir angles for upper canopy evaluation; and (c) maximizing sampling perspectives and viewing angles to reduce occlusion effects. Our results offer transferable insights to optimize UAV LiDAR data acquisitions, thereby contributing to an enhanced structural metric retrieval and improved analysis of forest functional properties.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104493"},"PeriodicalIF":7.6,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143767775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of Fall Armyworm infestation in maize fields during vegetative growth stages using temporal Sentinel-2","authors":"Tatenda Dzurume ,&nbsp;Roshanak Darvishzadeh ,&nbsp;Timothy Dube ,&nbsp;T.S. Amjath Babu ,&nbsp;Mutasim Billah ,&nbsp;Syed Nurul Alam ,&nbsp;Mustafa Kamal ,&nbsp;Md. Harun-Or-Rashid ,&nbsp;Badal Chandra Biswas ,&nbsp;Md. Ashraf Uddin ,&nbsp;Md. Abdul Muyeed ,&nbsp;Md. Mostafizur Rahman Shah ,&nbsp;Timothy J. Krupnik ,&nbsp;Andrew Nelson","doi":"10.1016/j.jag.2025.104516","DOIUrl":"10.1016/j.jag.2025.104516","url":null,"abstract":"<div><div>Fall Armyworm (FAW), Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae), poses a significant risk to global food and income security by attacking various crops, particularly maize. Early detection and management of FAW infestation are crucial for mitigating its impact on crop yields. This study investigated the effect of FAW infestation on the spectral signature of maize fields and classified infestation severity in Bangladesh using Sentinel-2 satellite imagery and Random Forest (RF) classification. Field observations on FAW infestation severity (none, moderate, and severe), collected by the Bangladesh Department of Agricultural Extension during 2019 and 2020, were used to train the RF classifier. Six thousand nine hundred ninety-eight observations were collected from 579 maize fields through weekly scouting. The Kruskal-Wallis test and Dunn’s post-hoc test were applied to identify the most significant spectral bands (P &lt; 0.05) for detecting FAW incidence and severity across different maize growth stages. The results demonstrated that the spectral reflectance from Sentinel-2 bands varied significantly among different classes of FAW infestation, with noticeable differences observed during the early developmental stages of maize (vegetative growth stages 3 to 8). RF identified nine spectral bands and two spectral vegetation indices as important for FAW infestation discrimination. The RF classifier was evaluated using five-fold cross-validation, achieving an overall accuracy between 74 % and 84 %. The independent test set’s accuracy ranged from 72 % to 82 %. The mean multiclass AUC ranged from 0.83 to 0.95. Moreover, the results demonstrated the feasibility of detecting the severity of FAW infestation using temporal Sentinel-2 data and machine learning techniques. These findings underscore the potential of remote sensing and machine learning techniques for effectively monitoring and managing crop pests. The study provides valuable insights for classifying FAW infestation using high-resolution multitemporal data.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104516"},"PeriodicalIF":7.6,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"D3GNN: Double dual dynamic graph neural network for multisource remote sensing data classification","authors":"Teng Yang ,&nbsp;Song Xiao ,&nbsp;Jiahui Qu","doi":"10.1016/j.jag.2025.104496","DOIUrl":"10.1016/j.jag.2025.104496","url":null,"abstract":"<div><div>Convolutional Neural Network (CNN) has garnered attention due to its outstanding performance in multisource remote sensing (RS) image classification. However, classical CNN-based methods primarily concentrate on information within a fixed-size neighborhood and a standard square region, neglecting long-range and global information. As non-Euclidean data, the topological structure enables flexible construction of relationships between objects, which can be served as an effective carrier of global information. Graph Neural Network (GNN), capable of extracting features from the topological structure, is considered as a solution for capturing global information. Nevertheless, GNN faces challenges as the manually defined static graph structure might not accurately capture the complexity of the data. We propose a double dual dynamic graph neural network (D<span><math><msup><mrow></mrow><mrow><mn>3</mn></mrow></msup></math></span>GNN) with dynamic topological structure refinement for multisource RS data classification. D<span><math><msup><mrow></mrow><mrow><mn>3</mn></mrow></msup></math></span>GNN generates multiple topological structures to achieve a comprehensive perception of scene features by utilizing local spatial information and distinctive data from various sources. Given the characteristics of heterogeneous-structure data, D<span><math><msup><mrow></mrow><mrow><mn>3</mn></mrow></msup></math></span>GNN implements targeted topological structure remodeling and refinement to overcome the limitations imposed by static graph, thereby enabling the network to generate feature embeddings with enhanced discriminative power. The experimental results show that D<span><math><msup><mrow></mrow><mrow><mn>3</mn></mrow></msup></math></span>GNN achieves superior performance compared to other current methods.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104496"},"PeriodicalIF":7.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combining multi-source data to investigate vessel wake temperature gradients and dynamic patterns","authors":"Mengqi Lyu ,&nbsp;Liyuan Li ,&nbsp;Wencong Zhang ,&nbsp;Long Gao ,&nbsp;Yifan Zhong ,&nbsp;Jingjie Jiao ,&nbsp;Xiaoyan Li ,&nbsp;Fansheng Chen","doi":"10.1016/j.jag.2025.104509","DOIUrl":"10.1016/j.jag.2025.104509","url":null,"abstract":"<div><div>The movement of a vessel generates cold and warm wake patterns with temperature gradients on the sea surface, which provide detection possibilities for satellite-based infrared detection systems. This work analyzes the temporal characteristics of ship wake dissipation on the sea surface, based on multi-source data from low Earth orbit satellite thermal infrared imaging, Automatic Identification System (AIS) data, and numerical simulation results, revealing the dynamic information contained within the thermal wake. The temperature images of sea surface thermal wakes generated by vessels at different speeds were obtained using numerical simulation methods. The thermal infrared characteristics of the surface vessel wakes were verified using images from the thermal imaging spectrometer aboard the SDGSAT-1 satellite. The simulation results reveal the patterns of generation, diffusion, and attenuation of the infrared thermal wake produced by moving vessels in the ocean. By combining simulations with infrared images from the SDGSAT-1 satellite, the thermal infrared temperature characteristics of wakes on the sea surface are summarized. This method overcomes the limitations of traditional optical monitoring techniques at night, while capturing more information on sea surface temperature variations. By deeply exploring sea surface thermal signature data, this paper provides technical support for all-weather vessel speed inversion using single-satellite data.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104509"},"PeriodicalIF":7.6,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}