Semi-supervised satellite image segmentation using spatial and temporally informed poisson learning

IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM(2023)

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Abstract
Satellite image segmentation is crucial in various fields, but acquiring a large amount of labeled data can be challenging. Often, remote sensing images are unlabeled due to the high cost of manual labeling, which hampers training effectiveness and generalization. To address this issue, we propose a spatially and temporally informed graph-based semi-supervised learning approach for satellite image segmentation based on Poisson learning. The main difference to traditional Poisson learning is that our distance function that we use to compute similarity between pixels considers spectral, spatial, and temporal information. Experimental results on the Sentinel-2 time series demonstrate that our approach outperforms other traditional approaches, achieving robust performance in remote sensing image segmentation, especially at a very low label rate.
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Key words
semi-supervised learning,remote sensing,time series,segmentation,graph learning
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