Bridging the Gap: Evaluating Transfer Learning Strategies for Block-Level Urban Land Use Classification on Regional Empirical High-Resolution Remote Sensing Imagery

Transfer learning, particularly through pre-training and fine-tuning strategies, is widely used to address the substantial training sample requirement of deep learning methods for urban land use classification using high spatial resolution remote sensing imagery. However, most studies utilize natura...

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Veröffentlicht in:	IEEE journal of selected topics in applied earth observations and remote sensing 2024-11, p.1-20
Hauptverfasser:	Zhang, Qingyang, Wang, Zhihua, Yang, Xiaomei, Lai, Feilin, Liu, Yueming, Gao, Ku, Luo, Haofeng
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Analytical models convolutional neural network Deep learning high-resolution remote sensing images Object oriented modeling Remote sensing Roads Sensors Testing Training Transfer learning Urban land use
Online-Zugang:	Volltext
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