Unsupervised Hyperspectral Unmixing via Nonlinear Autoencoders
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2021.3077833
Abstract: This work derives a novel unsupervised neural network-based scheme for unmixing hyperspectral pixels. A novel autoencoder structure was combined with a kernelization layer, mapping the mixed pixels in a higher dimensional space for easier separability,… read more here.
Keywords: structure; nonlinear mixing; unmixing via; via nonlinear ... See more keywords
A Distribution-Dependent Mumford–Shah Model for Unsupervised Hyperspectral Image Segmentation
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2022.3227061
Abstract: Hyperspectral images provide a rich representation of the underlying spectrum for each pixel, allowing for a pixelwise classification/segmentation into different classes. As the acquisition of labeled training data is very time-consuming, unsupervised methods become crucial… read more here.
Keywords: distribution dependent; mumford shah; segmentation; hyperspectral image ... See more keywords