Remote sensing scene classification with masked image modeling

Liya Wang; Alex Tien

doi:10.1117/12.2680898

17 October 2023 Remote sensing scene classification with masked image modeling

Liya Wang, Alex Tien

Proceedings Volume 12732, Microwave Remote Sensing: Data Processing and Applications II; 1273202 (2023) https://doi.org/10.1117/12.2680898
Event: SPIE Remote Sensing, 2023, Amsterdam, Netherlands

Abstract

Remote sensing scene classification has been extensively studied for its critical roles in geological survey, oil exploration, traffic management, earthquake prediction, wildfire monitoring, and intelligence monitoring. In the past, the Machine Learning (ML) methods for performing the task mainly used the backbones pretrained in the manner of supervised learning (SL). As Masked Image Modeling (MIM), a self-supervised learning (SSL) technique, has been shown as a better way for learning visual feature representation, it presents a new opportunity for improving ML performance on the scene classification task. This research aims to explore the potential of MIM pretrained backbones on four well-known classification datasets: Merced, AID, NWPU-RESISC45, and Optimal-31. Compared to the published benchmarks, we show that the MIM pretrained Vision Transformer (ViTs) backbones outperform other alternatives (up to 18% on top 1 accuracy) and that the MIM technique can learn better feature representation than the supervised learning counterparts (up to 5% on top 1 accuracy). Moreover, we show that the general-purpose MIM-pretrained ViTs can achieve competitive performance as the specially designed yet complicated Transformer for Remote Sensing (TRS) framework. Our experiment results also provide a performance baseline for future studies.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Liya Wang and Alex Tien "Remote sensing scene classification with masked image modeling", Proc. SPIE 12732, Microwave Remote Sensing: Data Processing and Applications II, 1273202 (17 October 2023); https://doi.org/10.1117/12.2680898

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