{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T08:36:26Z","timestamp":1768811786368,"version":"3.49.0"},"reference-count":58,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T00:00:00Z","timestamp":1669334400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62071030"],"award-info":[{"award-number":["62071030"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["ZR2022MD002"],"award-info":[{"award-number":["ZR2022MD002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2205cxzx040431"],"award-info":[{"award-number":["2205cxzx040431"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Shandong Province","award":["62071030"],"award-info":[{"award-number":["62071030"]}]},{"name":"Natural Science Foundation of Shandong Province","award":["ZR2022MD002"],"award-info":[{"award-number":["ZR2022MD002"]}]},{"name":"Natural Science Foundation of Shandong Province","award":["2205cxzx040431"],"award-info":[{"award-number":["2205cxzx040431"]}]},{"name":"Marine Project","award":["62071030"],"award-info":[{"award-number":["62071030"]}]},{"name":"Marine Project","award":["ZR2022MD002"],"award-info":[{"award-number":["ZR2022MD002"]}]},{"name":"Marine Project","award":["2205cxzx040431"],"award-info":[{"award-number":["2205cxzx040431"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Ship classification based on high-resolution synthetic aperture radar (SAR) imagery plays an increasingly important role in various maritime affairs, such as marine transportation management, maritime emergency rescue, marine pollution prevention and control, marine security situational awareness, and so on. The technology of deep learning, especially convolution neural network (CNN), has shown excellent performance on ship classification in SAR images. Nevertheless, it still has some limitations in real-world applications that need to be taken seriously by researchers. One is the insufficient number of SAR ship training samples, which limits the learning of satisfactory CNN, and the other is the limited information that SAR images can provide (compared with natural images), which limits the extraction of discriminative features. To alleviate the limitation caused by insufficient training datasets, one of the widely adopted strategies is to pre-train CNNs on a generic dataset with massive labeled samples (such as ImageNet) and fine-tune the pre-trained network on the target dataset (i.e., a SAR dataset) with a small number of training samples. However, recent studies have shown that due to the different imaging mechanisms between SAR and natural images, it is hard to guarantee that the pre-trained CNNs (even if they perform extremely well on ImageNet) can be finely tuned by a SAR dataset. On the other hand, to extract the most discriminative ship representation features from SAR images, the existing methods have carried out fruitful research on network architecture design, attention mechanism embedding, feature fusion, etc. Although these efforts improve the performance of SAR ship classification to some extent, they are usually based on more complex network architecture and higher dimensional features, accompanied by more time-consuming storage expenses. Through the analysis of SAR image characteristics and CNN feature extraction mechanism, this study puts forward three hypotheses: (1) Pre-training CNN on a task-specific dataset may be more effective than that on a generic dataset; (2) a shallow CNN may be more suitable for SAR image feature extraction than a deep one; and (3) the deep features extracted by CNNs can be further refined to improve the feature discrimination ability. To validate these hypotheses, we propose to learn a shallow CNN which is pre-trained on a task-specific dataset, i.e., the optical remote sensing ship dataset (ORS) instead of on the widely adopted ImageNet dataset. For comparison purposes, we designed 28 CNN architectures by changing the arrangement of the CNN components, the size of convolutional filters, and pooling formulations based on VGGNet models. To further reduce redundancy and improve the discrimination ability of the deep features, we propose to refine deep features by active convolutional filter selection based on the coefficient of variation (COV) sorting criteria. Extensive experiments not only prove that the above hypotheses are valid but also prove that the shallow network learned by the proposed pre-training strategy and the feature refining method can achieve considerable ship classification performance in SAR images like the state-of-the-art (SOTA) methods.<\/jats:p>","DOI":"10.3390\/rs14235986","type":"journal-article","created":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T07:01:30Z","timestamp":1669618890000},"page":"5986","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Ship Classification in SAR Imagery by Shallow CNN Pre-Trained on Task-Specific Dataset with Feature Refinement"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4859-1570","authenticated-orcid":false,"given":"Haitao","family":"Lang","sequence":"first","affiliation":[{"name":"College of Mathematics and Physics, Beijing University of Chemical Technology, Beijing 100029, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6767-1720","authenticated-orcid":false,"given":"Ruifu","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China"}]},{"given":"Shaoying","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences, Beijing 100871, China"}]},{"given":"Siwen","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Mathematics and Physics, Beijing University of Chemical Technology, Beijing 100029, China"}]},{"given":"Jialu","family":"Li","sequence":"additional","affiliation":[{"name":"College of Mathematics and Physics, Beijing University of Chemical Technology, Beijing 100029, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"National Research Council (2011). Critical infrastructure for ocean research and societal needs in 2030. Technical Report, National Academy of Sciences.","DOI":"10.2172\/1044991"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3129","DOI":"10.1109\/TGRS.2011.2112371","article-title":"Ship classification in single-pol SAR images based on fuzzy logic","volume":"49","author":"Margarit","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1562","DOI":"10.1109\/LGRS.2013.2262073","article-title":"Ship classification in TerraSAR-X images with feature space based sparse representation","volume":"10","author":"Xing","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1109\/LGRS.2016.2514482","article-title":"Ship classification based on superstructure scattering features in SAR images","volume":"13","author":"Jiang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lang, H., Wu, S., Lai, Q., and Ma, L. (2016, January 26\u201328). Capability of geometric features to classify ships in SAR imagery. Proceedings of the Image and Signal Processing for Remote Sensing XXII. International Society for Optics and Photonics, Edinburgh, UK.","DOI":"10.1117\/12.2241375"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1109\/LGRS.2015.2506570","article-title":"Ship classification in SAR image by joint feature and classifier selection","volume":"13","author":"Lang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1765","DOI":"10.1109\/LGRS.2017.2734889","article-title":"Ship Classification in Moderate-Resolution SAR Image by Naive Geometric Features-Combined Multiple Kernel Learning","volume":"14","author":"Lang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lin, H., Song, S., and Yang, J. (2018). Ship classification based on MSHOG feature and task-driven dictionary learning with structured incoherent constraints in SAR images. Remote Sens., 10.","DOI":"10.3390\/rs10020190"},{"key":"ref_9","unstructured":"Xu, Y., Lang, H., Chai, X., and Ma, L. (2018, January 10\u201312). Distance metric learning for ship classification in SAR images. Proceedings of the Image and Signal Processing for Remote Sensing XXIV. SPIE, Berlin, Germany."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2276","DOI":"10.1109\/JSTARS.2020.2991784","article-title":"Distribution shift metric learning for fine-grained ship classification in SAR images","volume":"13","author":"Xu","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhou, G., Zhang, G., and Xue, B. (2021). A maximum-information-minimum-redundancy-based feature fusion framework for ship classification in moderate-resolution SAR image. Sensors, 21.","DOI":"10.3390\/s21020519"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"036507","DOI":"10.1117\/1.JRS.16.036507","article-title":"Superstructure scattering features and their application in high-resolution SAR ship classification","volume":"16","author":"Wang","year":"2022","journal-title":"J. Appl. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4509504","DOI":"10.1109\/LGRS.2022.3183622","article-title":"Using Low-Resolution SAR Scattering Features for Ship Classification","volume":"19","author":"Salerno","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1109\/JOE.2017.2767106","article-title":"Ship classification in TerraSAR-X images with convolutional neural networks","volume":"43","author":"Bentes","year":"2017","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"035010","DOI":"10.1117\/1.JRS.12.035010","article-title":"Ship classification for unbalanced SAR dataset based on convolutional neural network","volume":"12","author":"Li","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Xi, Y., Xiong, G., and Yu, W. (2019, January 11\u201313). Feature-loss double fusion Siamese network for dual-polarized SAR ship classification. Proceedings of the 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP), Chongqing, China.","DOI":"10.1109\/ICSIDP47821.2019.9172933"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1080\/2150704X.2019.1650982","article-title":"Fine-grained ship classification based on deep residual learning for high-resolution SAR images","volume":"10","author":"Dong","year":"2019","journal-title":"Remote Sens. Lett."},{"key":"ref_18","first-page":"4002305","article-title":"SAR image classification using CNN embeddings and metric learning","volume":"19","author":"Li","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3022","DOI":"10.1109\/TGRS.2020.3009284","article-title":"Ship classification in medium-resolution SAR images via densely connected triplet CNNs integrating Fisher discrimination regularized metric learning","volume":"59","author":"He","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","first-page":"4011905","article-title":"Dual-polarized SAR ship grained classification based on CNN with hybrid channel feature loss","volume":"19","author":"Zeng","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","first-page":"4017205","article-title":"One-shot learning-based SAR ship classification using new hybrid Siamese network","volume":"19","author":"Raj","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xu, X., Zhang, X., and Zhang, T. (2021, January 11\u201316). Multi-Scale SAR Ship Classification with Convolutional Neural Network. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553116"},{"key":"ref_23","first-page":"4019905","article-title":"Squeeze-and-excitation Laplacian pyramid network with dual-polarization feature fusion for ship classification in sar images","volume":"19","author":"Zhang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, T., and Zhang, X. (2021). Injection of traditional hand-crafted features into modern CNN-based models for SAR ship classification: What, why, where, and how. Remote Sens., 13.","DOI":"10.3390\/rs13112091"},{"key":"ref_25","first-page":"5210322","article-title":"HOG-ShipCLSNet: A novel deep learning network with hog feature fusion for SAR ship classification","volume":"60","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"108365","DOI":"10.1016\/j.patcog.2021.108365","article-title":"A polarization fusion network with geometric feature embedding for SAR ship classification","volume":"123","author":"Zhang","year":"2022","journal-title":"Pattern Recognition"},{"key":"ref_27","first-page":"5231112","article-title":"C-SASO: A Clustering-Based Size-Adaptive Safer Oversampling Technique for Imbalanced SAR Ship Classification","volume":"60","author":"Li","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","first-page":"5222316","article-title":"Fast task-specific region merging for SAR image segmentation","volume":"60","author":"Ma","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4508405","DOI":"10.1109\/LGRS.2022.3178080","article-title":"Group Bilinear CNNs for Dual-Polarized SAR Ship Classification","volume":"19","author":"He","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhu, H., Guo, S., Sheng, W., and Xiao, L. (2022). SBNN: A Searched Binary Neural Network for SAR Ship Classification. Appl. Sci., 12.","DOI":"10.3390\/app12146866"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4509005","DOI":"10.1109\/LGRS.2022.3180793","article-title":"MetaBoost: A Novel Heterogeneous DCNNs Ensemble Network With Two-Stage Filtration for SAR Ship Classification","volume":"19","author":"Zheng","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1109\/JSTARS.2022.3142025","article-title":"SPAN: Strong Scattering Point Aware Network for Ship Detection and Classification in Large-Scale SAR Imagery","volume":"15","author":"Sun","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5214313","DOI":"10.1109\/TGRS.2021.3106915","article-title":"SAR Target Classification Using the Multikernel-Size Feature Fusion-Based Convolutional Neural Network","volume":"60","author":"Ai","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Meng, B., Min, M.J., Xi, Z., and Wang, L.G. (October, January 26). A High Resolution SAR Ship Sample Database and Ship Type Classification. Proceedings of the IEEE IGARSS 2020\u20132020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9323826"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"140303","DOI":"10.1007\/s11432-019-2772-5","article-title":"FUSAR-Ship: Building a high-resolution SAR-AIS matchup dataset of Gaofen-3 for ship detection and recognition","volume":"63","author":"Hou","year":"2020","journal-title":"Sci. China Inf. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Rizaev, I.G., and Achim, A. (2022). SynthWakeSAR: A Synthetic SAR Dataset for Deep Learning Classification of Ships at Sea. Remote Sens., 14.","DOI":"10.20944\/preprints202207.0450.v1"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Li, F.-F. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Oquab, M., Bottou, L., Laptev, I., and Sivic, J. (2014, January 23\u201328). Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.222"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, C., and Zhang, H. (2018). Ship classification in high-resolution SAR images using deep learning of small datasets. Sensors, 18.","DOI":"10.3390\/s18092929"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Lu, C., and Li, W. (2018). Ship classification in high-resolution SAR images via transfer learning with small training dataset. Sensors, 19.","DOI":"10.3390\/s19010063"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1109\/LGRS.2018.2792683","article-title":"Ship Classification in SAR Images Improved by AIS Knowledge Transfer","volume":"15","author":"Lang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Xu, Y., Lang, H., and Chai, X. (August, January 28). Distribution discrepancy maximization metric learning for ship classification in synthetic aperture radar images. Proceedings of the IEEE IGARSS 2019\u20132019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8899173"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1786","DOI":"10.1109\/LGRS.2019.2907139","article-title":"Discriminative adaptation regularization framework-based transfer learning for ship classification in SAR images","volume":"16","author":"Xu","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"6799","DOI":"10.1109\/TGRS.2020.3026387","article-title":"Ship classification in SAR images with geometric transfer metric learning","volume":"59","author":"Xu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5228814","DOI":"10.1109\/TGRS.2022.3178703","article-title":"Multi-Source Heterogeneous Transfer Learning via Feature Augmentation for Ship Classification in SAR Imagery","volume":"60","author":"Lang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","first-page":"4508105","article-title":"Semi-supervised Heterogeneous Domain Adaptation via Dynamic Joint Correlation Alignment Network for Ship Classification in SAR Imagery","volume":"19","author":"Lang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"4506405","DOI":"10.1109\/LGRS.2022.3162707","article-title":"Two-Stage Cross-Modality Transfer Learning Method for Military-Civilian SAR Ship Recognition","volume":"19","author":"Song","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"8038","DOI":"10.1109\/JSTARS.2022.3206753","article-title":"Improving Deep Subdomain Adaptation by Dual-Branch Network Embedding Attention Module for SAR Ship Classification","volume":"15","author":"Zhao","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_49","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, Harrahs and Harveys."},{"key":"ref_50","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"5455","DOI":"10.1007\/s10462-020-09825-6","article-title":"A survey of the recent architectures of deep convolutional neural networks","volume":"53","author":"Khan","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Boureau, Y.L., Bach, F., LeCun, Y., and Ponce, J. (2010, January 13\u201318). Learning mid-level features for recognition. Proceedings of the 2010 IEEE Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539963"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Xu, H., Chen, Y., Lin, R., and Kuo, C.C.J. (2017, January 12\u201315). Understanding CNN via deep features analysis. Proceedings of the Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Kuala Lumpur, Malaysia.","DOI":"10.1109\/APSIPA.2017.8282184"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Escorcia, V., Carlos Niebles, J., and Ghanem, B. (2015, January 7\u201312). On the relationship between visual attributes and convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298730"},{"key":"ref_57","unstructured":"Chollet, F. (2022, September 01). Keras. Available online: https:\/\/github.com\/fchollet\/keras."},{"key":"ref_58","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2016). TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/23\/5986\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:27:10Z","timestamp":1760146030000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/23\/5986"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,25]]},"references-count":58,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14235986"],"URL":"https:\/\/doi.org\/10.3390\/rs14235986","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,25]]}}}