{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T14:31:30Z","timestamp":1771079490850,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T00:00:00Z","timestamp":1733270400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62401473"],"award-info":[{"award-number":["62401473"]}]},{"name":"National Natural Science Foundation of China","award":["WR202404"],"award-info":[{"award-number":["WR202404"]}]},{"name":"National Natural Science Foundation of China","award":["G2024WD0159"],"award-info":[{"award-number":["G2024WD0159"]}]},{"name":"National Natural Science Foundation of China","award":["D5000240239"],"award-info":[{"award-number":["D5000240239"]}]},{"name":"National Natural Science Foundation of China","award":["HTKI2024KL504010"],"award-info":[{"award-number":["HTKI2024KL504010"]}]},{"name":"National Natural Science Foundation of China","award":["2021A1515110077"],"award-info":[{"award-number":["2021A1515110077"]}]},{"name":"National Natural Science Foundation of China","award":["2023JSQ0101"],"award-info":[{"award-number":["2023JSQ0101"]}]},{"name":"National Natural Science Foundation of China","award":["ZBKF-24-15"],"award-info":[{"award-number":["ZBKF-24-15"]}]},{"name":"National Key Laboratory of Unmanned Aerial Vehicle Technology in NPU","award":["62401473"],"award-info":[{"award-number":["62401473"]}]},{"name":"National Key Laboratory of Unmanned Aerial Vehicle Technology in NPU","award":["WR202404"],"award-info":[{"award-number":["WR202404"]}]},{"name":"National Key Laboratory of Unmanned Aerial Vehicle Technology in NPU","award":["G2024WD0159"],"award-info":[{"award-number":["G2024WD0159"]}]},{"name":"National Key Laboratory of Unmanned Aerial Vehicle Technology in NPU","award":["D5000240239"],"award-info":[{"award-number":["D5000240239"]}]},{"name":"National Key Laboratory of Unmanned Aerial Vehicle Technology in NPU","award":["HTKI2024KL504010"],"award-info":[{"award-number":["HTKI2024KL504010"]}]},{"name":"National Key Laboratory of Unmanned Aerial Vehicle Technology in NPU","award":["2021A1515110077"],"award-info":[{"award-number":["2021A1515110077"]}]},{"name":"National Key Laboratory of Unmanned Aerial Vehicle Technology in NPU","award":["2023JSQ0101"],"award-info":[{"award-number":["2023JSQ0101"]}]},{"name":"National Key Laboratory of Unmanned Aerial Vehicle Technology in NPU","award":["ZBKF-24-15"],"award-info":[{"award-number":["ZBKF-24-15"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["62401473"],"award-info":[{"award-number":["62401473"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["WR202404"],"award-info":[{"award-number":["WR202404"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["G2024WD0159"],"award-info":[{"award-number":["G2024WD0159"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["D5000240239"],"award-info":[{"award-number":["D5000240239"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["HTKI2024KL504010"],"award-info":[{"award-number":["HTKI2024KL504010"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2021A1515110077"],"award-info":[{"award-number":["2021A1515110077"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2023JSQ0101"],"award-info":[{"award-number":["2023JSQ0101"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["ZBKF-24-15"],"award-info":[{"award-number":["ZBKF-24-15"]}]},{"name":"National Key Laboratory Fund Project for Space Microwave Communication","award":["62401473"],"award-info":[{"award-number":["62401473"]}]},{"name":"National Key Laboratory Fund Project for Space Microwave Communication","award":["WR202404"],"award-info":[{"award-number":["WR202404"]}]},{"name":"National Key Laboratory Fund Project for Space Microwave Communication","award":["G2024WD0159"],"award-info":[{"award-number":["G2024WD0159"]}]},{"name":"National Key Laboratory Fund Project for Space Microwave Communication","award":["D5000240239"],"award-info":[{"award-number":["D5000240239"]}]},{"name":"National Key Laboratory Fund Project for Space Microwave Communication","award":["HTKI2024KL504010"],"award-info":[{"award-number":["HTKI2024KL504010"]}]},{"name":"National Key Laboratory Fund Project for Space Microwave Communication","award":["2021A1515110077"],"award-info":[{"award-number":["2021A1515110077"]}]},{"name":"National Key Laboratory Fund Project for Space Microwave Communication","award":["2023JSQ0101"],"award-info":[{"award-number":["2023JSQ0101"]}]},{"name":"National Key Laboratory Fund Project for Space Microwave Communication","award":["ZBKF-24-15"],"award-info":[{"award-number":["ZBKF-24-15"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["62401473"],"award-info":[{"award-number":["62401473"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["WR202404"],"award-info":[{"award-number":["WR202404"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["G2024WD0159"],"award-info":[{"award-number":["G2024WD0159"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["D5000240239"],"award-info":[{"award-number":["D5000240239"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["HTKI2024KL504010"],"award-info":[{"award-number":["HTKI2024KL504010"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2021A1515110077"],"award-info":[{"award-number":["2021A1515110077"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2023JSQ0101"],"award-info":[{"award-number":["2023JSQ0101"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["ZBKF-24-15"],"award-info":[{"award-number":["ZBKF-24-15"]}]},{"name":"Zhongdian Tian\u2019ao Innovation Theory and Technology Group Fund","award":["62401473"],"award-info":[{"award-number":["62401473"]}]},{"name":"Zhongdian Tian\u2019ao Innovation Theory and Technology Group Fund","award":["WR202404"],"award-info":[{"award-number":["WR202404"]}]},{"name":"Zhongdian Tian\u2019ao Innovation Theory and Technology Group Fund","award":["G2024WD0159"],"award-info":[{"award-number":["G2024WD0159"]}]},{"name":"Zhongdian Tian\u2019ao Innovation Theory and Technology Group Fund","award":["D5000240239"],"award-info":[{"award-number":["D5000240239"]}]},{"name":"Zhongdian Tian\u2019ao Innovation Theory and Technology Group Fund","award":["HTKI2024KL504010"],"award-info":[{"award-number":["HTKI2024KL504010"]}]},{"name":"Zhongdian Tian\u2019ao Innovation Theory and Technology Group Fund","award":["2021A1515110077"],"award-info":[{"award-number":["2021A1515110077"]}]},{"name":"Zhongdian Tian\u2019ao Innovation Theory and Technology Group Fund","award":["2023JSQ0101"],"award-info":[{"award-number":["2023JSQ0101"]}]},{"name":"Zhongdian Tian\u2019ao Innovation Theory and Technology Group Fund","award":["ZBKF-24-15"],"award-info":[{"award-number":["ZBKF-24-15"]}]},{"name":"Open Research Subject of State Key Laboratory of Intelligent Game","award":["62401473"],"award-info":[{"award-number":["62401473"]}]},{"name":"Open Research Subject of State Key Laboratory of Intelligent Game","award":["WR202404"],"award-info":[{"award-number":["WR202404"]}]},{"name":"Open Research Subject of State Key Laboratory of Intelligent Game","award":["G2024WD0159"],"award-info":[{"award-number":["G2024WD0159"]}]},{"name":"Open Research Subject of State Key Laboratory of Intelligent Game","award":["D5000240239"],"award-info":[{"award-number":["D5000240239"]}]},{"name":"Open Research Subject of State Key Laboratory of Intelligent Game","award":["HTKI2024KL504010"],"award-info":[{"award-number":["HTKI2024KL504010"]}]},{"name":"Open Research Subject of State Key Laboratory of Intelligent Game","award":["2021A1515110077"],"award-info":[{"award-number":["2021A1515110077"]}]},{"name":"Open Research Subject of State Key Laboratory of Intelligent Game","award":["2023JSQ0101"],"award-info":[{"award-number":["2023JSQ0101"]}]},{"name":"Open Research Subject of State Key Laboratory of Intelligent Game","award":["ZBKF-24-15"],"award-info":[{"award-number":["ZBKF-24-15"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Automatic modulation recognition (AMR) stands as a crucial core technology within the realm of signal processing and perception, playing a significant part in harsh electromagnetic environments. The time\u2013frequency image (TFI) of communication signals can manifest modulation characteristics and serve as a foundation for signal modulation recognition and classification. However, under the influence of the electromagnetic environment, communication signals are exposed to varying degrees of interference, which poses a challenge to the recognition of modulation types. Taking into account the effects of interference and channel fading, this paper introduces a communication signal modulation recognition algorithm based on deep learning (DL) and time\u2013frequency analysis. This approach employs short-time Fourier transform (STFT) to generate time\u2013frequency diagrams from time-domain signals. Subsequently, it binarizes the image and feeds it as input data to the neural network. Our research presents a composite deep convolutional neural network (CNN) architecture known as the composite dense-residual neural network (CDRNN). This architecture focuses on enhancing the feature extraction and identification, aiming to achieve accurate recognition of modulation types in harsh electromagnetic environments. Finally, simulation results validate that the proposed deep learning algorithm holds remarkable advantages in boosting the accuracy of modulation type recognition with better adaptability. The algorithm shows better performance even in harsh electromagnetic environments. When the signal-to-noise ratio (SNR) is 18 dB, the recognition accuracy can reach 92.1%.<\/jats:p>","DOI":"10.3390\/rs16234550","type":"journal-article","created":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T10:07:10Z","timestamp":1733306830000},"page":"4550","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["An Automatic Modulation Recognition Algorithm Based on Time\u2013Frequency Features and Deep Learning with Fading Channels"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1021-8225","authenticated-orcid":false,"given":"Xiaoya","family":"Zuo","sequence":"first","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, Xi\u2019an 710129, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7184-4042","authenticated-orcid":false,"given":"Yuan","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, Xi\u2019an 710129, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1396-3802","authenticated-orcid":false,"given":"Rugui","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, Xi\u2019an 710129, China"}]},{"given":"Ye","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, Xi\u2019an 710129, China"},{"name":"Research and Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen 518057, China"}]},{"given":"Lu","family":"Li","sequence":"additional","affiliation":[{"name":"China Electronics Technology Corporation 29th Research Institute, Chengdu 610036, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1049\/iet-com:20050176","article-title":"Survey of automatic modulation classification techniques: Classical approaches and new trends","volume":"1","author":"Dobre","year":"2007","journal-title":"IET Commun."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2516","DOI":"10.1109\/TAES.2019.2891155","article-title":"Deep Learning Based Radio-Signal Identification with Hardware Design","volume":"55","author":"Mendis","year":"2019","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104530","DOI":"10.1109\/ACCESS.2021.3099222","article-title":"Robust Automatic Modulation Recognition through Joint Contribution of Hand-Crafted and Contextual Features","volume":"9","author":"Jdid","year":"2021","journal-title":"IEEE Access."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"13243","DOI":"10.1109\/TVT.2020.3022394","article-title":"A Novel Deep Learning and Polar Transformation Frame work for an Adaptive Automatic Modulation Classification","volume":"69","author":"Ghasemzadeh","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"155584","DOI":"10.1109\/ACCESS.2021.3128508","article-title":"Artificial Intelligence-Driven Real-Time Automatic Modulation Classification Scheme for Next-Generation Cellular Networks","volume":"9","author":"Kaleem","year":"2021","journal-title":"IEEE Access"},{"key":"ref_6","first-page":"4573","article-title":"Deep Learning based Cooperative Automatic Modulation Classification Method for MIMO Systems","volume":"69","author":"Wang","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1109\/JSTSP.2018.2797022","article-title":"Over-the-Air Deep Learning Based Radio Signal Classification","volume":"12","author":"Roy","year":"2018","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"154290","DOI":"10.1109\/ACCESS.2020.3017641","article-title":"Automatic Modulation Classification Scheme Based on LSTM with Random Erasing and Attention Mechanism","volume":"8","author":"Chen","year":"2020","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"57851","DOI":"10.1109\/ACCESS.2021.3071801","article-title":"Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey","volume":"9","author":"Jdid","year":"2021","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2509","DOI":"10.1109\/LWC.2021.3105978","article-title":"Automatic Modulation Classification Based on Cauchy-score Constellation and Lightweight Network under Impulsive Noise","volume":"10","author":"Luan","year":"2021","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Doan, V.S., Huynh-The, T., Hua, C.H., Pham, Q.V., and Kim, D.S. (2020, January 7\u201311). Learning Constellation Map with Deep CNN for Accurate Modulation Recognition. Proceedings of the GLOBECOM 2020\u20142020 IEEE Global Communications Conference, Taipei, Taiwan.","DOI":"10.1109\/GLOBECOM42002.2020.9348129"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"7035","DOI":"10.1007\/s11042-023-15814-y","article-title":"Automatic modulation recognition using CNN deep learning models","volume":"83","author":"Mohsen","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1109\/TNNLS.2018.2850703","article-title":"Modulation Classification Based on Signal Constellation Diagrams and Deep Learning","volume":"30","author":"Peng","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liu, X., Wu, Z., and Tang, C. (2021, January 27\u201328). Modulation Recognition Algorithm Based on ResNet50 Multi-feature Fusion. Proceedings of the 2021 International Conference on Intelligent Transportation, Big Data and Smart City (ICITBS), Xi\u2019an, China.","DOI":"10.1109\/ICITBS53129.2021.00171"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Sun, D., Chen, Y., Liu, J., Li, Y., and Ma, R. (2019, January 6\u20139). Digital Signal Modulation Recognition Algorithm Based on VGGNet Model. Proceedings of the 2019 IEEE 5th International Conference on Computer and Communications (ICCC), Chengdu, China.","DOI":"10.1109\/ICCC47050.2019.9064328"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Valad\u00e3o, M., Silva, L., Serr\u00e3o, M., Guerreiro, W., Furtado, V., Freire, N., Monteiro, G., and Craveiro, C. (2023, January 6\u20138). MobileNetV3-based Automatic Modulation Recognition for Low-Latency Spectrum Sensing. Proceedings of the 2023 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA.","DOI":"10.1109\/ICCE56470.2023.10043380"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5160","DOI":"10.1109\/TMTT.2021.3112199","article-title":"Self-Attention Bi-LSTM Networks for Radar Signal Modulation Recognition","volume":"69","author":"Wei","year":"2021","journal-title":"IEEE Trans. Microw. Theory Tech."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1109\/LWC.2019.2900247","article-title":"Spectrum Analysis and Convolutional Neural Network for Automatic Modulation Recognition","volume":"8","author":"Zeng","year":"2019","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.aej.2024.06.008","article-title":"Automatic modulation recognition using deep CVCNN-LSTM architecture","volume":"12","author":"Cheng","year":"2024","journal-title":"Alex. Eng. J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1109\/TCCN.2023.3252580","article-title":"Toward the Automatic Modulation Classification with Adaptive Wavelet Network","volume":"9","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1042","DOI":"10.1109\/LCOMM.2020.2971216","article-title":"Detection of Frequency-Hopping Signals with Deep Learning","volume":"24","author":"Lee","year":"2020","journal-title":"IEEE Commun. Lett."},{"key":"ref_22","first-page":"469","article-title":"Automatic Modulation Classification Using Convolutional Neural Network with Features Fusion of SPWVD and BJD","volume":"5","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Signal Inf. Process. Netw."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"11074","DOI":"10.1109\/ACCESS.2017.2716191","article-title":"Convolutional Neural Net works for Automatic Cognitive Radio Waveform Recognition","volume":"5","author":"Zhang","year":"2017","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1109\/LWC.2022.3140828","article-title":"Learning of Time-Frequency Attention Mechanism for Automatic Modulation Recognition","volume":"11","author":"Lin","year":"2022","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1109\/LCOMM.2022.3197979","article-title":"A Time-Frequency Image Denoising Method via Neural Networks for Radar Waveform Recognition","volume":"27","author":"Hu","year":"2023","journal-title":"IEEE Commun. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ma, P., Liu, Y., Li, L., Zhu, Z., and Li, B. (2023). A Robust Constellation Diagram Representation for Communication Signal and Automatic Modulation Classification. Electronics, 12.","DOI":"10.3390\/electronics12040920"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Shi, L., Jiang, H., and Lin, Y. (2023, January 20\u201322). Modulation Recognition of Frequency Hopping Signal Based on Graph Convolutional Network. In Procceedings of the 2023 IEEE 23rd International Conference on Communication Technology (ICCT), Wuxi, China.","DOI":"10.1109\/ICCT59356.2023.10419205"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Dong, N., Jiang, H., Liu, Y., and Zhang, J. (2024). Intrapulse Modulation Radar Signal Recognition Using CNN with Second-Order STFT-Based Synchrosqueezing Transform. Remote Sens., 16.","DOI":"10.3390\/rs16142582"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"108989","DOI":"10.1016\/j.engappai.2024.108989","article-title":"Transformer-based models for intrapulse modulation recognition of radar waveforms","volume":"136","author":"Bhatti","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_30","unstructured":"Choi, D.Y., Kim, W.K., Kim, J.H., and Cho, H. (2016, January 5\u20138). Performance of analog and digital modulation schemes under sweep jamming. In Procceedings of the 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN), Vienna, Austria."},{"key":"ref_31","unstructured":"O\u2019Shea, T.J., Corgan, J., and Clancy, T.C. (2016). Convolutional radio modulation recognition networks. Engineering Applications of Neural Networks: 17th International Conference, EANN 2016, Aberdeen, UK, 2\u20135 September 2016, Springer."},{"key":"ref_32","unstructured":"Khan, M.J., Singh, I., and Tayal, S. (2022, January 23\u201324). BER Performance using BPSK Modulation over Rayleigh and Rician Fading Channel. In Procceedings of the 2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT), Indore, India."},{"key":"ref_33","first-page":"434","article-title":"An Improved Unscale S-Transform in Frequency Domain","volume":"20","author":"Zhang","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wang, X., Ying, T., and Tian, W. (2020, January 17\u201319). Spectrum Representation Based on STFT. In Procceedings of the 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Chengdu, China.","DOI":"10.1109\/CISP-BMEI51763.2020.9263516"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"103444","DOI":"10.1016\/j.dsp.2022.103444","article-title":"A lightweight and efficient neural network for modulation recognition","volume":"123","author":"Fengyuan","year":"2022","journal-title":"Digit. Signal Process."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 20\u201325). Coordinate Attention for Efficient Mobile Network Design. In Procceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_37","first-page":"100134","article-title":"Deep learning in computer vision: A critical review of emerging techniques and application scenarios","volume":"6","author":"Chai","year":"2021","journal-title":"Mach. Learn. Appl."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/23\/4550\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:46:58Z","timestamp":1760114818000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/23\/4550"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,4]]},"references-count":37,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["rs16234550"],"URL":"https:\/\/doi.org\/10.3390\/rs16234550","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,4]]}}}