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Semi-supervised Classification for Remote Sensing Datasets

  • Conference paper
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Image Analysis and Processing – ICIAP 2023 (ICIAP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14233))

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Abstract

Deep semi-supervised learning (DSSL) is a rapidly-growing field that takes advantage of a limited number of labeled examples to leverage massive amounts of unlabeled data. The underlying idea is that training on small yet well-selected examples can perform as effectively as a predictor trained on a larger number chosen at random [14]. In this study, we explore the most relevant approaches in DSSL literature like FixMatch [19], CoMatch [13], and, the class aware contrastive SSL (CCSSL) [25]. Our objective is to perform an initial comparative study of these methods and assess them on two remote sensing (RS) datasets: UCM [27] and AID [22]. The performance of these methods was determined based on their accuracy in comparison to a supervised benchmark. The results highlight that the CoMatch framework achieves the highest accuracy for both the UCM and AID datasets, with accuracies of 95.52% and 93.88% respectively. Importantly, all DSSL algorithms outperform the supervised benchmark, emphasizing their effectiveness in leveraging a limited number of labeled examples to enhance classification accuracy for remote sensing scene classification tasks. The code used in this study was adapted from CCSSL [25] and the detailed implementation will be accessible at https://github.com/itzahs/SSL-for-RS.

This work was supported by the Ministry of Science and Innovation of Spain (PID2021-128794OB-I00), and the University Jaume I (PREDOC/2020/50 and Becas de Estancia de Investigación E-2022-30).

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Authors and Affiliations

  1. Institute of New Imaging Technologies, University Jaume I, Castellón de la Plana, Spain

    Itza Hernandez-Sequeira & Filiberto Pla

  2. Department of Computer Science and Systems, University of Murcia, Murcia, Spain

    Ruben Fernandez-Beltran

  3. Institute of Advanced Research in Artificial Intelligence, Vienna, Austria

    Yonghao Xu & Pedram Ghamisi

  4. Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany

    Pedram Ghamisi

Authors
  1. Itza Hernandez-Sequeira
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  2. Ruben Fernandez-Beltran
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  3. Yonghao Xu
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  4. Pedram Ghamisi
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  5. Filiberto Pla
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Correspondence to Itza Hernandez-Sequeira .

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Editors and Affiliations

  1. University of Udine, Udine, Italy

    Gian Luca Foresti

  2. University of Udine, Udine, Italy

    Andrea Fusiello

  3. University of York, York, UK

    Edwin Hancock

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Hernandez-Sequeira, I., Fernandez-Beltran, R., Xu, Y., Ghamisi, P., Pla, F. (2023). Semi-supervised Classification for Remote Sensing Datasets. In: Foresti, G.L., Fusiello, A., Hancock, E. (eds) Image Analysis and Processing – ICIAP 2023. ICIAP 2023. Lecture Notes in Computer Science, vol 14233. Springer, Cham. https://doi.org/10.1007/978-3-031-43148-7_39

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  • DOI: https://doi.org/10.1007/978-3-031-43148-7_39

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