DS²DL learns latent representations of hyperspectral images via unsupervised masked autoencoders and performs superpixel-based diffusion clustering in that space to improve accuracy over prior methods.
Unsupervised feature learning using recurrent neural nets for segmenting hyperspectral images.IEEE Geoscience and Remote Sensing Letters, 18(12):2142–2146
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Deep Spatially-Regularized and Superpixel-Based Diffusion Learning for Unsupervised Hyperspectral Image Clustering
DS²DL learns latent representations of hyperspectral images via unsupervised masked autoencoders and performs superpixel-based diffusion clustering in that space to improve accuracy over prior methods.