Improving Remote Sensing Classification with Transfer Learning: Exploring the Impact of Heterogenous Transfer Learning

Deep learning (DL) has become increasingly popular in recent years, with researchers and businesses alike successfully applying it to a wide range of tasks. However, one challenge that DL faces in certain domains, such as remote sensing (RS), is the difficulty of creating large, well-annotated train...

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Bibliographic Details
Published in:Engineering proceedings 2023-10, Vol.56 (1), p.316
Main Authors: Maria Rouba, Mohammed El Amin Larabi
Format: Article
Language:English
Subjects:
heterogenous learning
homogenous learning
Landsat-9
multispectral remote sensing
Sentinel-2
transfer learning
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Summary:Deep learning (DL) has become increasingly popular in recent years, with researchers and businesses alike successfully applying it to a wide range of tasks. However, one challenge that DL faces in certain domains, such as remote sensing (RS), is the difficulty of creating large, well-annotated training datasets. This is due to the high cost of acquiring and labeling RS data. This challenge significantly limits the development of DL in RS. RS data can come from multiple sources, such as satellites, airplanes, and drones, and use different sensor technologies. Training DL models on data from one source may not produce the same accuracy on data from other sources, even if they cover the same region. Transfer learning (TL) can help to address this challenge by relaxing the requirement for large training datasets. Specifically, TL allows us to train a model on data from one source and then adapt it to data from another source, even with fewer training data. This makes TL a promising approach for solving both the problem of multisource adaptation and the problem of insufficient training data in the target domain. This paper evaluates the homogenous and heterogeneous TL approach that addresses model transfer across different domains. Transfer gain is measured through specific statistical metrics such as precision, kappa, recall, and F1-score, and a positive gain is empirically shown in the vast majority of cases. The proposed method is evaluated on the challenging task of Multispectral RS image (MSI) classification due to the complexity and variety of natural scenes. This work is examined in terms of its social, economic, and environmental consequences. Additionally, potential future directions for research and the achievement of established goals are explored.
ISSN:2673-4591
DOI:10.3390/ASEC2023-15505