This paper proposes a hyperspectral unmixing algorithm using auto-NMF based on the L-curve theory. It is an approach to automatically estimate regularization ...
with Automatically Estimating Regularization Parameters∗ ... regularization parameters while these parameters are usually important to the performance of NMF.
May 20, 2022 · Abstract—Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corre- sponding abundances from a ...
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Oct 9, 2020 · In our work, we propose an NMF based unmixing framework which jointly uses a handcrafting regularizer and a learnt regularizer from data. we ...
Jun 2, 2023 · Hyperspectral unmixing (HU) is a key means of making sufficient use of remotely sensed hyperspectral image (HSI) data, which aims to separate ...
Bibliographic details on Hyperspectral unmixing using non-negative matrix factorization with automatically estimating regularization parameters.
Dec 8, 2023 · Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from a ...
The invention provides an L-curve-based hyperspectral unmixing method for estimating a regularized parameter automatically. An expected target is reached by ...
We introduce a Nonnegative Matrix Factorization (NMF) model with a regularization function that encourages a low-rank representation of data.
ABSTRACT. Spectral unmixing is an important task for remotely sensed hyperspectral data exploitation. It expresses each.