Enhancing crop segmentation in satellite image time-series with transformer networks

I. Gallo; M. Gatti; N. Landro; C. Loschiavo; M. Boschetti; R. La Grassa; A. U. Rehman

doi:10.1117/12.3023389

3 April 2024 Enhancing crop segmentation in satellite image time-series with transformer networks

I. Gallo, M. Gatti, N. Landro, C. Loschiavo, M. Boschetti, R. La Grassa, A. U. Rehman

Proceedings Volume 13072, Sixteenth International Conference on Machine Vision (ICMV 2023); 1307208 (2024) https://doi.org/10.1117/12.3023389
Event: Sixteenth International Conference on Machine Vision (ICMV 2023), 2023, Yerevan, Armenia

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 validation accuracy of 96.14% and a test accuracy of 95.26% on the Munich dataset, surpassing the previous best results of 93.55% for validation and 92.94% for the test. Additionally, the model’s performance on the Lombardia dataset is comparable to UNet3D and superior to FPN and DeepLabV3. Experiments of this study indicate that the model will likely achieve comparable or superior accuracy to CNNs while requiring significantly less training time. These findings highlight the potential of transformer-based architectures for crop segmentation in SITS, opening new avenues for remote sensing applications.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

I. Gallo, M. Gatti, N. Landro, C. Loschiavo, M. Boschetti, R. La Grassa, and A. U. Rehman "Enhancing crop segmentation in satellite image time-series with transformer networks", Proc. SPIE 13072, Sixteenth International Conference on Machine Vision (ICMV 2023), 1307208 (3 April 2024); https://doi.org/10.1117/12.3023389

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