Domain-Adversarial Training of Self-Attention-Based Networks for Land Cover Classification Using Multi-Temporal Sentinel-2 Satellite Imagery

Domain-Adversarial Training of Self-Attention-Based Networks for Land Cover Classification Using Multi-Temporal Sentinel-2 Satellite Imagery

The increasing availability of large-scale remote sensing labeled data has prompted researchers to develop increasingly precise and accurate data-driven models for land cover and crop classification (LC&CC). Moreover, with the introduction of self-attention and introspection mechanisms, deep lea...

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Bibliographic Details
Main Authors: Mauro Martini, Vittorio Mazzia, Aleem Khaliq, Marcello Chiaberge
Format: Article
Language:English
Published: MDPI AG 2021-06-01
Series:Remote Sensing
Subjects:
domain adaptation
Transformers
deep learning
land cover classification
Online Access:https://www.mdpi.com/2072-4292/13/13/2564

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