CGAP: A Hybrid Contrastive and Graph-Based Active Learning Pipeline to Detect Water and Sediment in Multispectral Images

CGAP: A Hybrid Contrastive and Graph-Based Active Learning Pipeline to Detect Water and Sediment in Multispectral Images

In this article, we develop a contrastive graph-based active learning pipeline (CGAP) to identify surface water and near-water sediment pixels in multispectral images. The CGAP enhances the graph-based active learning pipeline (Chen et al., 2023), which outperforms methods such as CNN-Unet, support...

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Bibliographic Details
Main Authors: Bohan Chen, Kevin Miller, Andrea L. Bertozzi, Jon Schwenk
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Active learning
contrastive learning
graph learning
remote sensing
surface water detection
Online Access:https://ieeexplore.ieee.org/document/10747084/

Internet

https://ieeexplore.ieee.org/document/10747084/

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