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

{"title":"SSMM: Semi-supervised manifold method with spatial-spectral self-training and regularized metric constraints for hyperspectral image dimensionality reduction","authors":"Bei Zhu ,&nbsp;Yao Jin ,&nbsp;Xuehua Guan ,&nbsp;Yanni Dong","doi":"10.1016/j.jag.2025.104373","DOIUrl":"10.1016/j.jag.2025.104373","url":null,"abstract":"<div><div>Manifold learning is an important technique for dimensionality reduction in hyperspectral images. It maps data from high dimensions to low dimensions to eliminate redundant information. However, the existing manifold learning methods cannot effectively solve the problem of lacking label information and ignore the negative impact of dimensionality reduction on sample division. To address these, we propose a semi-supervised manifold method with spatial-spectral self-training and regularized metric constraints (SSMM) for hyperspectral image dimensionality reduction. The spatial-spectral self-training module is proposed, which learns pseudo-labels by jointly training the spatial and spectral information. This module first locates the spatial neighbors of the labeledit can adapt to different data distributions and feature samples and then sets an adaptive threshold based on the spectral features of labeled samples to filter spatial neighbors, so as to obtain the spatial-spectral neighbors as pseudo-labeled samples. In addition, to divide the sample categories while dimensionality reduction, low-dimensional manifold embedding is constructed and the metric constraint is imposed on the manifolds. Specifically, the Gaussian kernel function based on Mahalanobis distance is used to map the data into a more discriminative low-dimensional manifold embedding. At the same time, the regularized distance metric constraint is imposed on the manifold, so that samples of the same class are clustered and different classes are mutually exclusive. SSMM conducts various forms of experiments on the Houston 2013, Indian Pines, and Washington DC datasets. In the dimensionality reduction experiments, the overall accuracy of SSMM in any dimension is higher than that of other algorithms. In the classification experiments, the KAPPA coefficient of SSMM on the three data sets is improved by 1.41%, 0.61%, and 0.27% respectively. The feature extraction experiments show superior clustering performance. These experimental results demonstrate that SSMM not only effectively solves the problem of insufficient label information, but also significantly improves the classification accuracy of hyperspectral images after dimensionality reduction, which is superior to the existing manifold learning methods.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104373"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350537","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":"Dual Fine-Grained network with frequency Transformer for change detection on remote sensing images","authors":"Zhen Li ,&nbsp;Zhenxin Zhang ,&nbsp;Mengmeng Li ,&nbsp;Liqiang Zhang ,&nbsp;Xueli Peng ,&nbsp;Rixing He ,&nbsp;Leidong Shi","doi":"10.1016/j.jag.2025.104393","DOIUrl":"10.1016/j.jag.2025.104393","url":null,"abstract":"<div><div>Change detection is a fundamental yet challenging task in remote sensing, crucial for monitoring urban expansion, land use changes, and environmental dynamics. However, compared with common color images, objects in remote sensing images exhibit minimal interclass variation and significant intraclass variation in the spectral dimension, with obvious scale inconsistency in the spatial dimension. Change detection complexity presents significant challenges, including differentiating similar objects, accounting for scale variations, and identifying pseudo changes. This research introduces a dual fine-grained network with a frequency Transformer (named as FTransDF-Net) to address the above issues. Specifically, for small-scale and approximate spectral ground objects, the network employs an encoder-decoder architecture consisting of dual fine-grained gated (DFG) modules. This enables the extraction and fusion of fine-grained level information in dual dimensions of features, facilitating a comprehensive analysis of their differences and correlations. As a result, a dynamic fusion representation of salient information is achieved. Additionally, we develop a lightweight frequency transformer (LFT) with minimal parameters for detecting large-scale ground objects that undergo significant changes over time. This is achieved by incorporating a frequency attention (FA) module, which utilizes Fourier transform to model long-range dependencies and combines global adaptive attentive features with multi-level fine-grained features. Our comparative experiments across four publicly available datasets demonstrate that FTransDF-Net reaches advanced results. Importantly, it outperforms the leading comparison method by 1.23% and 2.46% regarding IoU metrics concerning CDD and DSIFN, respectively. Furthermore, efficacy for each module is substantiated through ablation experiments. The code is accessible on <span><span>https://github.com/LeeThrzz/FTrans-DF-Net</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104393"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143372456","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":"CSTN: A cross-region crop mapping method integrating self-training and contrastive domain adaptation","authors":"Shuwen Peng ,&nbsp;Liqiang Zhang ,&nbsp;Rongchang Xie ,&nbsp;Ying Qu","doi":"10.1016/j.jag.2025.104379","DOIUrl":"10.1016/j.jag.2025.104379","url":null,"abstract":"<div><div>Crop mapping is essential for agricultural management and food production monitoring, but challenges like limited crop labels and poor model generalization significantly hinder large-scale crop mapping. Here, we introduce a novel Contrastive Self-Training Network (CSTN), integrating a self-training strategy and contrastive domain adaptation (CDA) for cross-region crop mapping. CSTN uses pseudo-labels in the target region generated by the self-training strategy to assist supervised learning, and aligns features across regions using class-aware prototypes. Qualitative and quantitative evaluations demonstrate that CSTN significantly outperforms state-of-the-art methods with a 12.29 % increase in average F1-score, particularly in maize identification. Moreover, CSTN also enables early-season crop classification for pre-harvest decision-making applications. The interpretability of the model is demonstrated through an in-depth analysis of feature map visualizations, attention map visualizations, and the effectiveness of the modules. This study provides a robust method for enhancing large-scale crop mapping and facilitating more accurate and timely agricultural practices.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104379"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377086","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":"SD-Mamba: A lightweight synthetic-decompression network for cross-modal flood change detection","authors":"Yu Shen,&nbsp;Shuang Yao,&nbsp;Zhenkai Qiang,&nbsp;Guanxiang Pei","doi":"10.1016/j.jag.2025.104409","DOIUrl":"10.1016/j.jag.2025.104409","url":null,"abstract":"<div><div>Cross-modal flood change detection using optical and SAR images has become one of the most commonly used techniques for monitoring the progression of flooding events. Existing methods fail to adequately capture the interrelationship between semantics and changes, which limits the potential for effective flood detection. To address this issue, we propose a lightweight Synthetic-decompression network. The synthetic component is divided into four stages, each of which employs a Multi-branch Asymmetric Part-convolution block (MAPC) and a Temporal Semantic Interaction module (TSIM) to extract semantic features from dual-temporal images. Subsequently, these features are fed into the Temporal-mamba (T-Mamba), which uses 4D Selective Scanning (SS4D) to traverse temporal change information in four directions. The decompression component employs a three-stage Asymmetric Coordinate-convolution block (ACoord-Conv) to project the change results onto the source images, thereby indirectly supervising the model’s detection performance. Compared to the 22 state-of-the-art (SOTA) lightweight methods, SD-Mamba achieves an optimal balance between computational efficiency and detection accuracy. Under the same computational conditions, SD-Mamba demonstrated superior performance to other Mamba-based models, with an improvement of 1.01% in mIoU, while maintaining a lightweight structure with only 5.32M parameters and 12.24G floating-point operations (FLops). The code is available at <span><span>https://github.com/yaoshuang-yaobo/SD-Mamba</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104409"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402957","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":"Generating MODIS hourly land surface temperature under clear sky conditions using Fourier series analysis","authors":"Hadi Zare Khormizi ,&nbsp;Mohammad Jafari ,&nbsp;Hamidreza Ghafarian Malamiri ,&nbsp;Ali Tavili ,&nbsp;Hamidreza Keshtkar","doi":"10.1016/j.jag.2024.104341","DOIUrl":"10.1016/j.jag.2024.104341","url":null,"abstract":"<div><div>Land surface temperature (LST) data with high temporal and spatial resolution are used in many studies, e.g. to assess climate changes, land–atmosphere interactions, surface energy balance, etc. However, clouds and the limitations of geostationary and polar orbiting satellites hinder the collection of high-quality thermal infrared (TIR) data. This research aims to generate hourly LST data from the Moderate Resolution Imaging Spectroradiometer (MODIS) with four daily observations. The Multi-channel Singular Spectrum Analysis (M−SSA) algorithm was used to reconstruct lost data due to clouds in the MODIS annual LST time series. Subsequently, Fourier series analysis was employed to generate hourly LST data based on the four MODIS observations per day. The developed Fourier series model was evaluated using hourly LST data from Meteosat-9 and ground surface soil temperature data at eight different Ameriflux sites. The evaluation of the Fourier series model showed that the Root Mean Square Error (RMSE) and coefficient of determination (R²) between the hourly LST data from the Meteosat-9 satellite and the hourly LST data generated by the Fourier series model using four simultaneous MODIS observations averaged 1.70 Kelvin and 0.98, respectively, throughout Iran. For Ameriflux sites, the average RMSE and R2 were 1.15 K and 0.98 between the surface soil temperature data and the surface soil temperature data generated using four simultaneous MODIS observations per day, respectively. Notably, the highest RMSE was observed during sunrise and sunset.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104341"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142902114","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":"BSG-WSL: BackScatter-guided weakly supervised learning for water mapping in SAR images","authors":"Kai Wang,&nbsp;Zhongle Ren,&nbsp;Biao Hou,&nbsp;Weibin Li,&nbsp;Licheng Jiao","doi":"10.1016/j.jag.2025.104385","DOIUrl":"10.1016/j.jag.2025.104385","url":null,"abstract":"<div><div>Extracting and analyzing water resources in Synthetic Aperture Radar (SAR) images is crucial for flood management and environmental resource planning due to the ability to monitor ground all-weather and all-time. However, extracting water entirely from high-resolution SAR images in diverse scenarios is challenging due to variable water shapes, many low-intensity land covers similar to water, and scarce labels. In this article, a BackScatter-Guided Weakly Supervised Learning (BSG-WSL) framework based on image-level labels is proposed for water extraction with the requirement of high generalization and low labeling. In BSG-WSL, a BackScatter-Guided Network (BSGNet) is proposed, where the backscatter information of water is used to guide the feature extraction process, yielding precise Class Attention Maps (CAMs) of water. Then, a morphological pseudo-label optimization algorithm is designed to employ CAMs to generate high-quality pseudo-labels. Finally, a confidence cross-entropy loss is introduced to utilize pseudo-labels to train the extraction model and achieve precise water extraction in different scenarios. Experiments on three datasets of SAR images from the GF-3 and Sentinel-1B satellites verify that the proposed method achieves state-of-the-art performance compared to other weakly supervised methods based on image-level annotations.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104385"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143083363","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":"Deep learning reveals hotspots of global oceanic oxygen changes from 2003 to 2020","authors":"Dongliang Ma ,&nbsp;Fang Zhao ,&nbsp;Likai Zhu ,&nbsp;Xiaofei Li ,&nbsp;Jine Wei ,&nbsp;Xi Chen ,&nbsp;Lijun Hou ,&nbsp;Ye Li ,&nbsp;Min Liu","doi":"10.1016/j.jag.2025.104363","DOIUrl":"10.1016/j.jag.2025.104363","url":null,"abstract":"<div><div>The decrease in global oceanic dissolved oxygen (DO) has exerted a profound impact on marine ecosystems and biogeochemical processes. However, our comprehension of DO distribution and its global change patterns remains hindered by sparse measurements and coarse-resolution simulations. Here we presented Oxyformer, a deep learning method that accurately learns DO-related information and estimates high-resolution global DO concentration. The results derived by Oxyformer demonstrate an accelerated decline in global oceanic DO content, estimated at approximately 1045 ± 665 Tmol decade⁻¹ from 2003 to 2020. The observed trends exhibit considerable variability across different regions and depths, with some new hotspots of recent DO change including the Equatorial Indian Ocean, the South Pacific Ocean, the North Atlantic Ocean, and the Western Coast of California. The unprecedented modeling approach provides a powerful tool to track changes in global DO contents and to facilitate the understanding of their influences on ocean ecosystems and biogeochemical processes.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104363"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990324","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":"IceEB: An ensemble-based method to map river ice type from radar images","authors":"Plante Lévesque Valérie,&nbsp;Chokmani Karem,&nbsp;Gauthier Yves,&nbsp;Bernier Monique","doi":"10.1016/j.jag.2024.104317","DOIUrl":"10.1016/j.jag.2024.104317","url":null,"abstract":"<div><div>This paper introduces IceEB, i.e., an innovative ensemble-based method that is designed to automate mapping of river ice types using radar imagery. Its goal is the merger of outcomes from three classifiers (IceMAP-R, RIACT, and IceBC) through ensemble-estimation, resulting in a highly performant and fully automated river ice-type map, which is applicable under all meteorological conditions. The first step of our research is the development of a <em>meta</em>-classifier and a confidence estimation index, then we validate our method using ground-truth datasets and finally compare the performance between IceEB and the original classifiers. The anticipated outcome was a map exhibiting superior results compared to individual classifiers. Validation and comparison of IceEB employed six RADARSAT-2 HH-HV C-band images that were selected from historical datasets of Quebec and Alberta rivers (Canada). IceEB integrates RADARSAT-2 satellite imagery, a digital elevation model, and a river mask, undergoing preprocessing tasks before activating the three initial classifiers. The <em>meta</em>-classifier then performs ensemble-based classification, yielding a legend comprised of water, sheet ice and rubble ice. This approach facilitates broad participation in validation data collection, differentiation between ice covers and ice jams, and minimization of assumptions regarding ice formation. We conclude that IceEB successfully combines existing radar remote sensing ice- classification models to create accurate river ice-type maps. IceEB’s ensemble-based approach outperforms individual classifiers, achieving overall accuracy &gt;91 % for each class. Shortcomings of the original classifiers are effectively offset through parallel use, resulting in marked improvements in automation and generalizability across diverse Canadian meteorological conditions.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104317"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049659","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":"Assessment and validation of Meteosat SEVIRI fire radiative power (FRP) retrievals over Kruger National Park","authors":"Gareth Roberts ,&nbsp;Martin. J. Wooster ,&nbsp;Tercia Strydom","doi":"10.1016/j.jag.2025.104375","DOIUrl":"10.1016/j.jag.2025.104375","url":null,"abstract":"<div><div>Satellite burned area, active fire and fire radiative power (FRP), are key to quantifying fire activity and are one of 54 essential climate variables (ECV) and it is important to validate these data to ensure their consistency. This study investigates some of the factors that influence FRP retrieval and uses Meteosat Spinning Enhanced Visible and InfraRed Imager (SEVIRI) data to do so. Analysis of the influence of a fire’s location within a SEVIRI pixel on FRP was carried out using fire simulations which indicate that FRP varies by up to 14 % at nadir for a single sensor and by up to 55 % when intercomparing simulated FRP from different SEVIRI sensors. Intercomparison between actual MET-11 and MET-08 FRP data on a per-pixel basis reveals a high degree of scatter (81.9 MW), strong correlation (R = 0.72), low bias (∼1 MW) and an average percentage difference of 15.7 %. Variability is reduced when aggregated to fire ‘clusters’ which improves the correlation (R = 0.96) and reduces the average percentage difference (4.2 %). Validation of MET-08 and MET-11 FRP retrievals using FRP from helicopter mounted longwave infrared (LWIR) and midwave infrared (MWIR) thermal cameras is carried out over five prescribed burns. The results reveal good agreement between the SEVIRI and thermal camera FRP although the SEVIRI FRP is typically overestimated compared to that from the LWIR camera. This study illustrates some of the challenges validating satellite FRP which should be accounted for when defining uncertainty thresholds for product requirements and in developing FRP validation protocols.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104375"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143050027","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":"Synergistic mapping of urban tree canopy height using ICESat-2 data and GF-2 imagery","authors":"Xiaodi Xu ,&nbsp;Ya Zhang ,&nbsp;Peng Fu ,&nbsp;Chaoya Dang ,&nbsp;Bowen Cai ,&nbsp;Qingwei Zhuang ,&nbsp;Zhenfeng Shao ,&nbsp;Deren Li ,&nbsp;Qing Ding","doi":"10.1016/j.jag.2024.104348","DOIUrl":"10.1016/j.jag.2024.104348","url":null,"abstract":"<div><div>Mapping urban top of canopy height (UTCH) is essential for quantifying urban vegetation carbon storage and developing effective vegetation management strategies. However, the scarcity and uneven distribution of urban measurement samples pose significant challenges to accurately estimating UTCH on a large scale in complex urban environments. To address this issue, this study utilized ICESat-2 photon spot height data as reference samples, in conjunction with high-resolution GF-2 remote sensing data, to estimate UTCH. To achieve UTCH mapping at a resolution of 4 m, a synergistic model integrating data from the GF-2 and ICESat-2 grid-based canopy height was constructed using the Random Forest technique. The model’s performance was evaluated using 111 urban tree canopy height samples collected across different urban areas. The experimental results demonstrated a moderate correlation between estimated and actual canopy heights, with a coefficient of determination (<em>R</em>) = 0.53, root mean square error (<em>RMSE</em>) = 2.9 m, and mean absolute error (<em>MAE</em>) = 2.04 m. Texture information, the red band, and MNDVI are key indicators for determining UTCH, with contribution percentages of 25.29 %, 13.7 %, and 25.75 %, respectively. As a result, the UTCH model created by fusing remote sensing spectral data with satellite-based lidar data can accurately estimate UTCH and offer a practical solution for predicting UTCH on a regional or even global scale.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104348"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143083301","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}