1 citations · 3 across the 5 of their papers we have counts for
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Unsupervised Spatial-spectral Hyperspectral Image Reconstruction and Clustering with Diffusion Geometry
Kangning Cui, Ruoning Li, Sam L. Polk +3
Hyperspectral images, which store a hundred or more spectral bands of reflectance, have become an important data source in natural and social sciences. Hyperspectral images are oft…
Active Diffusion and VCA-Assisted Image Segmentation of Hyperspectral Images
Sam L. Polk, Kangning Cui, Robert J. Plemmons +1
Hyperspectral images encode rich structure that can be exploited for material discrimination by machine learning algorithms. This article introduces the Active Diffusion and VCA-As…
Deep Diffusion Processes for Active Learning of Hyperspectral Images
Abiy Tasissa, Duc Nguyen, James Murphy
A method for active learning of hyperspectral images (HSI) is proposed, which combines deep learning with diffusion processes on graphs. A deep variational autoencoder extracts smo…
Hyperspectral Image Clustering with Spatially-Regularized Ultrametrics
Shukun Zhang, James M. Murphy
We propose a method for the unsupervised clustering of hyperspectral images based on spatially regularized spectral clustering with ultrametric path distances. The proposed method…
Spectral-Spatial Diffusion Geometry for Hyperspectral Image Clustering
James M. Murphy, Mauro Maggioni
An unsupervised learning algorithm to cluster hyperspectral image (HSI) data is proposed that exploits spatially-regularized random walks. Markov diffusions are defined on the spac…