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.
Learning long-term dependencies with gradient descent is difficult.IEEE Transactions on Neural Networks, 5(2):157–166, March 1994
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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.