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Abstract: Hyperspectral unmixing is an important image interpre-tation technique that aims to estimate the pure constituent materials (endmembers) and their ...
This note is about the energy of regular graphs. The energy of a graph is the sum of the absolute values of the eigenvalues of its adjacency matrix.
Jan 31, 2023 · The nonnegative matrix factorization (NMF) technique has been widely adopted in the hyperspectral images unmixing problem due to its own ...
Abstract—Spectral unmixing is an important tool in hyperspectral data analysis for estimating endmembers and abundance fractions in a mixed pixel.
Jun 15, 2023 · In [16], graph-regularized L1/2-NMF (GLNMF) minimizes the spectral correlation between pixels by construct- ing an adjacency matrix. Furthermore ...
Spectral unmixing is an important tool in hyperspectral data analysis for estimating endmembers and abundance fractions in a mixed pixel.
Graph Regularized Nonnegative Matrix Factorization for Hyperspectral Data Unmixing ... spatial and spectral contents in mixed-pixel decomposition of hyperspectral ...
May 20, 2022 · In. [31], graph Laplacian regularization was utilized to promote the smoothness of abundance maps in the sparse regression framework. These ...
Jun 16, 2021 · Hyperspectral image unmixing is an important task for remote sensing image processing. It aims at decomposing the mixed pixel of the image ...
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Jun 15, 2022 · Abstract—This paper proposes a weighted residual nonnegative matrix factorization (NMF) with spatial regularization to unmix hyperspectral ...