11th Workshop on Hyperspectral Image and Signal Processing: Evolutions in Remote Sensing (WHISPERS), Amsterdam, Hollanda, 24 - 26 Mart 2021, ss.1-4
This paper proposes a manifold based
approach for change detection in multitemporal hyperspectral images. Manifold
representation, using Laplacian Eigenmaps, is applied for dimensionality
reduction on stacked temporal datasets and change detection on the reduced
datasets. The resulting latent vectors are utilized to cluster the changed vs.
unchanged regions. A semi-supervised scheme is also proposed which circumvents
the challenging thresholding issue and enables satisfactory binary change
detection outputs. The proposed approach is validated on two real bitemporal
hyperspectral datasets.