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arXiv · 2306.10440

Graph-based Active Learning for Surface Water and Sediment Detection in Multispectral Images

Bohan Chen·Kevin Miller·Andrea L. Bertozzi·Jon Schwenk
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Abstract

We develop a graph active learning pipeline (GAP) to detect surface water and in-river sediment pixels in satellite images. The active learning approach is applied within the training process to optimally select specific pixels to generate a hand-labeled training set. Our method obtains higher accuracy with far fewer training pixels than both standard and deep learning models. According to our experiments, our GAP trained on a set of 3270 pixels reaches a better accuracy than the neural network method trained on 2.1 million pixels.

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Bohan Chen, Kevin Miller, Andrea L. Bertozzi, Jon Schwenk. 2023-06-17. Graph-based Active Learning for Surface Water and Sediment Detection in Multispectral Images. https://arxiv.org/abs/2306.10440

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