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
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