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
Material Identification in Complex Environments: Neural Network Approaches to Hyperspectral Image Analysis
This study examines plastic identification in complex hyperspectral scenes, where illumination, background materials, spectral mixing, and within-class variation make pixel-level classification substantially harder than controlled laboratory sorting. The data include both visible and near-infrared measurements, making it possible to compare the practical value of different wavelength ranges.
Oct 1, 2023