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Self-Paced Nonnegative Matrix Factorization for Hyperspectral Unmixing by Jiangtao Peng; Yicong Zhou; Weiwei Sun; Qian Du; Lekang Xia is a Engineering article available to read on EtoBox.

What is Self-Paced Nonnegative Matrix Factorization for Hyperspectral Unmixing about?

The presence of mixed pixels in the hyperspectral data makes unmixing to be a key step for many applications. Unsupervised unmixing needs to estimate the number of endmembers, their spectral signatures, and their abundances at each pixel. Since both endmember and abundance matrices are unknown, unsupervised unmixing can be considered as a blind source separation problem and can be solved by nonnegative matrix factorization (NMF). However, most of the existing NMF unmixing methods use a least-squares objective function that is sensitive to the noise and outliers. To deal with different types of noises in hyperspectral data, such as the noise in different bands (band noise), the noise in different pixels (pixel noise), and the noise in different elements of hyperspectral data matrix (element noise), we propose three self-paced learning based NMF (SpNMF) unmixing models in this article. The SpNMF models replace the least-squares loss in the standard NMF model with weighted least-squares losses and adopt a self-paced learning (SPL) strategy to learn the weights adaptively. In each iteration of SPL, atoms (bands or pixels or elements) with weight zero are considered as complex atoms and

Who reads Self-Paced Nonnegative Matrix Factorization for Hyperspectral Unmixing?

Author

Jiangtao Peng; Yicong Zhou; Weiwei Sun; Qian Du; Lekang Xia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2021

Language

EN

Field

Engineering (Physical Sciences)