IEEE

A Tensor-Based Go Decomposition Method for Hyperspectral Anomaly Detection

Abstract: Hyperspectral anomaly detection (HAD) aims at effectively separating the anomaly target from the background. The low-rank and sparse matrix decomposition (LRaSMD) technique has shown great ...
Scientific Research Publishing

Shiga, M. and Muto, S. (2019) Non-Negative Matrix Factorization and Its Extensions for Spectral Image Data Analysis. e-Journal of Surface Science and Nanotechnology, 17, 148-154.

ABSTRACT: In this paper, an Optimal Predictive Modeling of Nonlinear Transformations “OPMNT” method has been developed while using Orthogonal Nonnegative Matrix Factorization “ONMF” with the ...
IEEE

An Innovative Low-Rank Sparse Matrix Decomposition Clutter Suppression Method Based on Non-Convex Alternatives for Pseudo-Random-Coded Ground-Penetrating Radar

Abstract: This article analyzes the composition and characteristics of echo signals in a pseudorandom-coded ground-penetrating radar (GPR). Based on these characteristics, an innovative low-rank ...
Frontiers

Geochemical-process extraction and interpretation using matrix factorization: a framework for verifying effectiveness through forward modeling and inversion analysis

Matrix factorization techniques, such as principal component analysis (PCA) and independent component analysis (ICA), are widely used to extract geological processes from geochemical data. However, ...
GitHub

LaFA: Latent Feature Attacks on Non-negative Matrix Factorization

As Machine Learning (ML) applications rapidly grow, concerns about adversarial attacks compromising their reliability have gained significant attention. One unsupervised ML method known for its ...
Frontiers

Coastline target detection based on UAV hyperspectral remote sensing images

Timely and accurate monitoring of typical coastal targets using remote sensing technology is crucial for maintaining marine ecological stability. Hyperspectral target detection technology proves to be ...