arXiv Open Access 2023

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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Abstrak

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.

Topik & Kata Kunci

eess.IV

Penulis (4)

Bohan Chen

Kevin Miller

Andrea L. Bertozzi

Jon Schwenk

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APA MLA BibTeX

Chen, B., Miller, K., Bertozzi, A.L., Schwenk, J. (2023). Graph-based Active Learning for Surface Water and Sediment Detection in Multispectral Images. https://arxiv.org/abs/2306.10440

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Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓