May 27, 2024, 4:45 a.m. | Soham Chitnis, Kiran Mantripragada, Faisal Z. Qureshi

cs.LG updates on

arXiv:2311.10701v2 Announce Type: replace-cross
Abstract: The hyperspectral pixel unmixing aims to find the underlying materials (endmembers) and their proportions (abundances) in pixels of a hyperspectral image. This work extends the Latent Dirichlet Variational Autoencoder (LDVAE) pixel unmixing scheme by taking into account local spatial context while performing pixel unmixing. The proposed method uses an isotropic convolutional neural network with spatial attention to encode pixels as a dirichlet distribution over endmembers. We have evaluated our model on Samson, Hydice Urban, Cuprite, …

abstract arxiv attention autoencoder context convolutional cs.lg eess.iv image materials pixel pixels replace spatial type while work

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