{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T14:53:15Z","timestamp":1781621595320,"version":"3.54.5"},"reference-count":67,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T00:00:00Z","timestamp":1702512000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJWIS"],"published-print":{"date-parts":[[2024,2,23]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of clustered ground objects and noisy backgrounds. Recent research typically leverages larger volume models to achieve advanced performance. However, the operating environments of remote sensing commonly cannot provide unconstrained computational and storage resources. It requires lightweight algorithms with exceptional generalization capabilities.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>This study introduces an efficient knowledge distillation (KD) method to build a lightweight yet precise convolutional neural network (CNN) classifier. This method also aims to substantially decrease the training time expenses commonly linked with traditional KD techniques. This approach entails extensive alterations to both the model training framework and the distillation process, each tailored to the unique characteristics of RSIs. In particular, this study establishes a robust ensemble teacher by independently training two CNN models using a customized, efficient training algorithm. Following this, this study modifies a KD loss function to mitigate the suppression of non-target category predictions, which are essential for capturing the inter- and intra-similarity of RSIs.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>This study validated the student model, termed KD-enhanced network (KDE-Net), obtained through the KD process on three benchmark RSI data sets. The KDE-Net surpasses 42 other state-of-the-art methods in the literature published from 2020 to 2023. Compared to the top-ranked method\u2019s performance on the challenging NWPU45 data set, KDE-Net demonstrated a noticeable 0.4% increase in overall accuracy with a significant 88% reduction in parameters. Meanwhile, this study\u2019s reformed KD framework significantly enhances the knowledge transfer speed by at least three times.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>This study illustrates that the logit-based KD technique can effectively develop lightweight CNN classifiers for RSI classification without substantial sacrifices in computation and storage costs. Compared to neural architecture search or other methods aiming to provide lightweight solutions, this study\u2019s KDE-Net, based on the inherent characteristics of RSIs, is currently more efficient in constructing accurate yet lightweight classifiers for RSI classification.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ijwis-10-2023-0192","type":"journal-article","created":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T01:22:36Z","timestamp":1702344156000},"page":"129-158","source":"Crossref","is-referenced-by-count":8,"title":["Efficient knowledge distillation for remote sensing image classification: a CNN-based approach"],"prefix":"10.1108","volume":"20","author":[{"given":"Huaxiang","family":"Song","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chai","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhou","family":"Yong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","published-online":{"date-parts":[[2023,12,14]]},"reference":[{"key":"key2024022204050081700_ref001","doi-asserted-by":"publisher","first-page":"14078","DOI":"10.1109\/ACCESS.2021.3051085","article-title":"Classification of remote sensing images using efficientnet-B3 CNN model with attention","volume":"9","year":"2021","journal-title":"IEEE Access"},{"key":"key2024022204050081700_ref002","doi-asserted-by":"publisher","first-page":"103208","DOI":"10.1016\/j.jag.2023.103208","article-title":"Point cloud voxel classification of aerial urban LiDAR using voxel attributes and random forest approach","volume":"118","year":"2023","journal-title":"International Journal of Applied Earth Observation and Geoinformation"},{"issue":"8","key":"key2024022204050081700_ref003","doi-asserted-by":"publisher","first-page":"2212","DOI":"10.3390\/rs15082212","article-title":"TPENAS: a Two-Phase evolutionary neural architecture search for remote sensing image classification","volume":"15","year":"2023","journal-title":"Remote Sensing"},{"key":"key2024022204050081700_ref004","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3160492","article-title":"Remote sensing image scene classification using multiscale feature fusion covariance network with octave convolution","volume":"60","year":"2022","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"issue":"3","key":"key2024022204050081700_ref005","doi-asserted-by":"publisher","first-page":"516","DOI":"10.3390\/rs13030516","article-title":"Vision transformers for remote sensing image classification","volume":"13","year":"2021","journal-title":"Remote Sensing"},{"key":"key2024022204050081700_ref006","doi-asserted-by":"publisher","first-page":"10915","DOI":"10.1109\/CVPR52688.2022.01065","article-title":"Knowledge distillation: a good teacher is patient and consistent","volume-title":"2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Presented at the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE","year":"2022"},{"key":"key2024022204050081700_ref007","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3201755","article-title":"All grains, one scheme (AGOS): learning multigrain instance representation for aerial scene classification","volume":"60","year":"2022","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"key2024022204050081700_ref008","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2021.3097938","article-title":"Searching for CNN architectures for remote sensing scene classification","volume":"60","year":"2022","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"key2024022204050081700_ref009","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1145\/1150402.1150464","article-title":"Model compression","volume-title":"Presented at the Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, Philadelphia, PA","year":"2006"},{"issue":"3","key":"key2024022204050081700_ref010","doi-asserted-by":"publisher","first-page":"2471","DOI":"10.1007\/s11063-022-11072-5","article-title":"Scene level image classification: a literature review","volume":"55","year":"2023","journal-title":"Neural Processing Letters"},{"issue":"5","key":"key2024022204050081700_ref011","doi-asserted-by":"publisher","first-page":"719","DOI":"10.3390\/rs10050719","article-title":"Training small networks for scene classification of remote sensing images via knowledge distillation","volume":"10","year":"2018","journal-title":"Remote Sensing"},{"issue":"22","key":"key2024022204050081700_ref012","doi-asserted-by":"publisher","first-page":"3727","DOI":"10.3390\/electronics11223727","article-title":"RSCNet: an efficient remote sensing scene classification model based on lightweight convolution neural networks","volume":"11","year":"2022","journal-title":"Electronics"},{"key":"key2024022204050081700_ref013","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1109\/TIP.2021.3127851","article-title":"Remote sensing scene classification via multi-branch local attention network","volume":"31","year":"2022","journal-title":"IEEE Transactions on Image Processing"},{"issue":"17","key":"key2024022204050081700_ref014","doi-asserted-by":"publisher","first-page":"4423","DOI":"10.3390\/rs14174423","article-title":"Remote sensing scene image classification based on mms CNN\u2013HMM with stacking ensemble model","volume":"14","year":"2022","journal-title":"Remote Sensing"},{"key":"key2024022204050081700_ref015","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2021.3109061","article-title":"When CNNs meet vision transformer: a joint framework for remote sensing scene classification","volume":"19","year":"2022","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"key":"key2024022204050081700_ref016","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/3-540-45014-9_1","article-title":"Ensemble methods in machine learning","volume-title":"Multiple Classifier Systems","year":"2000"},{"key":"key2024022204050081700_ref017","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.isprsjprs.2023.01.014","article-title":"Current trends in deep learning for earth observation: an open-source benchmark arena for image classification","volume":"197","year":"2023","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing"},{"issue":"6","key":"key2024022204050081700_ref018","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","article-title":"Knowledge distillation: a survey","volume":"129","year":"2021","journal-title":"International Journal of Computer Vision"},{"key":"key2024022204050081700_ref019","doi-asserted-by":"publisher","first-page":"2546","DOI":"10.1109\/JSTARS.2022.3158703","article-title":"Remote sensing image scene classification by multiple granularity semantic learning","volume":"15","year":"2022","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"key":"key2024022204050081700_ref020","doi-asserted-by":"publisher","first-page":"103268","DOI":"10.1016\/j.jag.2023.103268","article-title":"Water-land classification for single-wavelength airborne LiDAR bathymetry based on waveform feature statistics and point cloud neighborhood analysis","volume":"118","year":"2023","journal-title":"International Journal of Applied Earth Observation and Geoinformation"},{"key":"key2024022204050081700_ref021","unstructured":"Hinton, G., Vinyals, O. and Dean, J. (2015), \u201cDistilling the knowledge in a neural network\u201d, available at: http:\/\/arxiv.org\/abs\/1503.02531 (accessed 30 June 2023)."},{"key":"key2024022204050081700_ref022","first-page":"33716","article-title":"Knowledge distillation from a stronger teacher","volume-title":"Advances in Neural Information Processing Systems","year":"2022"},{"issue":"14","key":"key2024022204050081700_ref023","doi-asserted-by":"publisher","first-page":"3645","DOI":"10.3390\/rs15143645","article-title":"Faster and better: a lightweight transformer network for remote sensing scene classification","volume":"15","year":"2023","journal-title":"Remote Sensing"},{"issue":"2\/3","key":"key2024022204050081700_ref024","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1108\/IJWIS-04-2022-0088","article-title":"An efficient model for copy-move image forgery detection","volume":"18","year":"2022","journal-title":"International Journal of Web Information Systems"},{"key":"key2024022204050081700_ref025","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2021.3093914","article-title":"Gated recurrent multiattention network for VHR remote sensing image classification","volume":"60","year":"2022","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"key2024022204050081700_ref026","first-page":"23818","article-title":"Efficient training of visual transformers with small datasets","volume-title":"Advances in Neural Information Processing Systems","year":"2021"},{"key":"key2024022204050081700_ref027","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3157671","article-title":"SCViT: a spatial-channel feature preserving vision transformer for remote sensing image scene classification","volume":"60","year":"2022","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"key2024022204050081700_ref028","doi-asserted-by":"publisher","first-page":"2223","DOI":"10.1109\/JSTARS.2022.3155665","article-title":"Homo\u2013heterogenous transformer learning framework for RS scene classification","volume":"15","year":"2022","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"issue":"86","key":"key2024022204050081700_ref029","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","year":"2008","journal-title":"Journal of Machine Learning Research"},{"key":"key2024022204050081700_ref030","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2023.3244565","article-title":"Multigranularity decoupling network with pseudolabel selection for remote sensing image scene classification","volume":"61","year":"2023","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"key2024022204050081700_ref031","doi-asserted-by":"publisher","first-page":"3962","DOI":"10.1109\/CVPR.2019.00409","article-title":"Relational knowledge distillation","volume-title":"2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Presented at the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE","year":"2019"},{"key":"key2024022204050081700_ref032","doi-asserted-by":"publisher","first-page":"10425","DOI":"10.1109\/CVPR42600.2020.01044","article-title":"Designing network design spaces","volume-title":"2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Presented at the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE","year":"2020"},{"key":"key2024022204050081700_ref033","unstructured":"Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C. and Bengio, Y. (2015), \u201cFitNets: hints for thin deep nets\u201d, available at: http:\/\/arxiv.org\/abs\/1412.6550 (accessed 30 June 2023)."},{"issue":"2","key":"key2024022204050081700_ref034","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: Visual explanations from deep networks via gradient-based localization","volume":"128","year":"2020","journal-title":"International Journal of Computer Vision"},{"issue":"1","key":"key2024022204050081700_ref035","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1002\/widm.1143","article-title":"Generating ensembles of heterogeneous classifiers using stacked generalization","volume":"5","year":"2015","journal-title":"WIREs Data Mining and Knowledge Discovery"},{"key":"key2024022204050081700_ref036","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3186588","article-title":"Remote sensing scene classification based on attention-enabled progressively searching","volume":"60","year":"2022","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"issue":"9","key":"key2024022204050081700_ref037","doi-asserted-by":"publisher","first-page":"2042","DOI":"10.3390\/rs14092042","article-title":"An attention cascade global\u2013local network for remote sensing scene classification","volume":"14","year":"2022","journal-title":"Remote Sensing"},{"issue":"1","key":"key2024022204050081700_ref038","doi-asserted-by":"publisher","first-page":"161","DOI":"10.3390\/rs14010161","article-title":"A lightweight convolutional neural network based on group-wise hybrid attention for remote sensing scene classification","volume":"14","year":"2021","journal-title":"Remote Sensing"},{"issue":"3","key":"key2024022204050081700_ref039","doi-asserted-by":"publisher","first-page":"545","DOI":"10.3390\/rs14030545","article-title":"Remote sensing scene image classification based on self-compensating convolution neural network","volume":"14","year":"2022","journal-title":"Remote Sensing"},{"issue":"2","key":"key2024022204050081700_ref040","doi-asserted-by":"publisher","first-page":"1381","DOI":"10.32604\/iasc.2023.039315","article-title":"A consistent mistake in remote sensing images\u2019 classification literature","volume":"37","year":"2023","journal-title":"Intelligent Automation and Soft Computing"},{"issue":"3","key":"key2024022204050081700_ref041","doi-asserted-by":"publisher","first-page":"1","DOI":"10.33166\/AETiC.2023.03.001","article-title":"A leading but simple classification method for remote sensing images","volume":"7","year":"2023","journal-title":"Annals of Emerging Technologies in Computing"},{"key":"key2024022204050081700_ref042","doi-asserted-by":"publisher","DOI":"10.1108\/IJICC-07-2023-0198","article-title":"MBC-net: long-range enhanced feature fusion for classifying remote sensing images","year":"2023","journal-title":"International Journal of Intelligent Computing and Cybernetics"},{"issue":"15\/16","key":"key2024022204050081700_ref043","doi-asserted-by":"publisher","first-page":"5976","DOI":"10.1080\/01431161.2021.2019851","article-title":"GSCCTL: a general semi-supervised scene classification method for remote sensing images based on clustering and transfer learning","volume":"43","year":"2022","journal-title":"International Journal of Remote Sensing"},{"issue":"4","key":"key2024022204050081700_ref044","doi-asserted-by":"publisher","first-page":"1600","DOI":"10.3934\/nhm.2023070","article-title":"Simple is best: a single-CNN method for classifying remote sensing images","volume":"18","year":"2023","journal-title":"Networks and Heterogeneous Media"},{"key":"key2024022204050081700_ref045","first-page":"6906","article-title":"Does knowledge distillation really work","volume-title":"Advances in Neural Information Processing Systems","year":"2021"},{"key":"key2024022204050081700_ref046","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","volume-title":"Proceedings of the 36th International Conference on Machine Learning","year":"2019"},{"key":"key2024022204050081700_ref047","doi-asserted-by":"publisher","first-page":"2030","DOI":"10.1109\/JSTARS.2021.3051569","article-title":"Attention consistent network for remote sensing scene classification","volume":"14","year":"2021","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"key":"key2024022204050081700_ref048","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2022.3185088","article-title":"LaST: label-free self-distillation contrastive learning with transformer architecture for remote sensing image scene classification","volume":"19","year":"2022","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"key":"key2024022204050081700_ref049","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3190934","article-title":"Transferring CNN with adaptive learning for remote sensing scene classification","volume":"60","year":"2022","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"issue":"7","key":"key2024022204050081700_ref050","doi-asserted-by":"publisher","first-page":"1773","DOI":"10.3390\/rs15071773","article-title":"P2FEViT: Plug-and-Play CNN feature embedded hybrid vision transformer for remote sensing image classification","volume":"15","year":"2023","journal-title":"Remote Sensing"},{"key":"key2024022204050081700_ref051","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3176603","article-title":"An empirical study of remote sensing pretraining","volume":"61","year":"2023","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"issue":"10","key":"key2024022204050081700_ref052","doi-asserted-by":"publisher","first-page":"1741","DOI":"10.1109\/LGRS.2020.3007775","article-title":"A lightweight intrinsic mean for remote sensing classification with lie group kernel function","volume":"18","year":"2021","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"key":"key2024022204050081700_ref053","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3152566","article-title":"Vision transformer: an excellent teacher for guiding small networks in remote sensing image scene classification","volume":"60","year":"2022","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"key2024022204050081700_ref054","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2021.3075712","article-title":"Remote sensing image scene classification based on global\u2013local dual-branch structure model","volume":"19","year":"2022","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"issue":"10","key":"key2024022204050081700_ref055","doi-asserted-by":"publisher","first-page":"5751","DOI":"10.1109\/TNNLS.2021.3071369","article-title":"Deep feature aggregation framework driven by graph convolutional network for scene classification in remote sensing","volume":"33","year":"2022","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"key2024022204050081700_ref056","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2020.3048024","article-title":"A lightweight and robust lie group-convolutional neural networks joint representation for remote sensing scene classification","volume":"60","year":"2022","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"key2024022204050081700_ref057","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2023.3265361","article-title":"An explainable spatial\u2013frequency multiscale transformer for remote sensing scene classification","volume":"61","year":"2023","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"key2024022204050081700_ref058","doi-asserted-by":"publisher","first-page":"7130","DOI":"10.1109\/CVPR.2017.754","article-title":"A gift from knowledge distillation: Fast optimization, network minimization and transfer learning","volume-title":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Presented at the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE","year":"2017"},{"issue":"7","key":"key2024022204050081700_ref059","doi-asserted-by":"publisher","first-page":"1999","DOI":"10.3390\/s20071999","article-title":"An efficient and lightweight convolutional neural network for remote sensing image scene classification","volume":"20","year":"2020","journal-title":"Sensors"},{"key":"key2024022204050081700_ref060","first-page":"6023","article-title":"CutMix: Regularization strategy to train strong classifiers with localizable features","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE, Seoul, Korea","year":"2019"},{"issue":"20","key":"key2024022204050081700_ref061","doi-asserted-by":"publisher","first-page":"4143","DOI":"10.3390\/rs13204143","article-title":"TRS: transformers for remote sensing scene classification","volume":"13","year":"2021","journal-title":"Remote Sensing"},{"key":"key2024022204050081700_ref062","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3192321","article-title":"LHNet: Laplacian convolutional block for remote sensing image scene classification","volume":"60","year":"2022","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"key2024022204050081700_ref063","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2023.3265346","article-title":"Local and long-range collaborative learning for remote sensing scene classification","volume":"61","year":"2023","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"issue":"5","key":"key2024022204050081700_ref064","doi-asserted-by":"publisher","first-page":"2308","DOI":"10.1109\/TNNLS.2021.3106391","article-title":"MGML: multigranularity multilevel feature ensemble network for remote sensing scene classification","volume":"34","year":"2023","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"key2024022204050081700_ref065","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2021.3126770","article-title":"Embedded self-distillation in compact multibranch ensemble network for remote sensing scene classification","volume":"60","year":"2022","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"issue":"11","key":"key2024022204050081700_ref066","doi-asserted-by":"publisher","first-page":"1926","DOI":"10.1109\/LGRS.2020.3011405","article-title":"Remote sensing image scene classification based on an enhanced attention module","volume":"18","year":"2021","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"issue":"1","key":"key2024022204050081700_ref067","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1109\/TCE.2022.3214382","article-title":"Neural augmented exposure interpolation for two large-exposure-ratio images","volume":"69","year":"2023","journal-title":"IEEE Transactions on Consumer Electronics"}],"container-title":["International Journal of Web Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJWIS-10-2023-0192\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJWIS-10-2023-0192\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T22:24:21Z","timestamp":1753395861000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/ijwis\/article\/20\/2\/129-158\/1215306"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,14]]},"references-count":67,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,12,14]]},"published-print":{"date-parts":[[2024,2,23]]}},"alternative-id":["10.1108\/IJWIS-10-2023-0192"],"URL":"https:\/\/doi.org\/10.1108\/ijwis-10-2023-0192","relation":{},"ISSN":["1744-0084","1744-0084"],"issn-type":[{"value":"1744-0084","type":"print"},{"value":"1744-0084","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,14]]}}}