CGAP: A Hybrid Contrastive and Graph-based Active Learning Pipeline to Detect Water and Sediment in Multispectral Images
The contrastive graph-based active learning pipeline (CGAP) combines a learned feature embedding with graph Laplace learning to classify land, surface water, and near-water sediment in Landsat imagery. Custom contrastive augmentations make the features robust to geometric transformations, changes in spatial resolution, and light cloud cover while reducing the dimension used for graph construction.
Jun 7, 2024
Batch Active Learning for Multispectral and Hyperspectral Image Segmentation Using Similarity Graphs
This paper develops a graph-based pipeline for label-efficient multispectral and hyperspectral image segmentation. Pixels or local image patches are embedded as nodes of a similarity graph, and graph Laplace learning propagates the small number of queried labels across the image.
Jun 1, 2024