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CGAP: A Hybrid Contrastive and Graph-based Active Learning Pipeline to Detect Water and Sediment in Multispectral Images
  • +1
  • Bohan Chen,
  • Kevin Miller,
  • Andrea L Bertozzi,
  • Jon Schwenk
Bohan Chen

Corresponding Author:[email protected]

Author Profile
Kevin Miller
Andrea L Bertozzi
Jon Schwenk

Abstract

We develop a contrastive graph-based active learning pipeline (CGAP) to identify surface water and nearwater sediment pixels in multispectral images. CGAP enhances the graph-based active learning pipeline (GAP) designed for surface water and sediment detection in multispectral imagery (10.1109/IGARSS52108.2023.10282009), which outperformed conventional methods such as CNN-Unet, SVM, and RF. Our improvements focus on boosting both the pipeline's robustness and efficiency by integrating a feature-embedding neural network prior to graph construction. Trained using contrastive learning, this neural network projects high-dimensional raw features into a lower-dimensional space, facilitating more efficient graph learning. The training process incorporates specialized augmentations to bolster the embedded features' resilience to geometric transformations, varying resolutions, and light cloud cover. Moreover, we develop a Python-based demo, GraphRiverClassifier (GRC), that uses Google Earth Engine and our enhanced pipeline to provide a user-friendly tool for rapid and accurate surface water and sediment analyses and rapid testing of algorithm performances.
31 May 2024Submitted to TechRxiv
07 Jun 2024Published in TechRxiv