Enhancing crop segmentation in satellite image time-series with transformer networks
Ignazio Gallo,
Mattia Gatti,
Nicola Landro
et al.
Abstract:Recent studies have shown that Convolutional Neural Networks (CNNs) achieve impressive results in crop segmentation of Satellite Image Time-Series (SITS). However, the emergence of transformer networks in various vision tasks raises the question of whether they can outperform CNNs in crop segmentation of SITS. This paper presents a revised version of the Transformer-based Swin UNETR model adapted specifically for crop segmentation of SITS. The proposed model demonstrates significant advancements, achieving a v… Show more
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