TNRG #4: MultiHU-TD: Multi-feature Hyperspectral Unmixing Based on Tensor Decomposition.
Автор: Tensor_Network_rg
Загружено: 2023-11-14
Просмотров: 97
Описание:
Mohamad Jouni's talk on Multi-feature Hyperspectral Unmixing Based on Tensor Decomposition in tensor network reading group at Mila
Abstract:
Hyperspectral unmixing allows to represent mixed pixels as a set of pure materials weighted by their abundances. Spectral features alone are often insufficient, so it is common to rely on other features of the scene. Matrix models become insufficient when the hyperspectral image is represented as a high-order tensor with additional features in a multimodal, multi-feature framework. Tensor models such as Canonical polyadic decomposition allow for this kind of unmixing, but lack a general framework and interpretability of the results. In this talk, he will explain an interpretable methodological framework for low-rank Multi-feature hyperspectral unmixing based on tensor decomposition (MultiHU-TD) which incorporates the abundance sum-to-one constraint in the Alternating optimization ADMM algorithm.
Повторяем попытку...
Доступные форматы для скачивания:
Скачать видео
-
Информация по загрузке:
