Open AccessHyperspectral Anomaly Detection Based on Subspace Low-Rank Decomposition
Author(s) -
Sheng-Ming Wang,
Tao Wang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1881/2/022011
Subject(s) - hyperspectral imaging , anomaly detection , pattern recognition (psychology) , subspace topology , artificial intelligence , noise reduction , noise (video) , computer science , anomaly (physics) , norm (philosophy) , mathematics , gaussian noise , algorithm , image (mathematics) , physics , political science , law , condensed matter physics
This paper mainly studies the anomaly detection algorithm on the basis of denoising and reconstruction of hyperspectral image, combined with some basic methods, such as subspace representation, tensor decomposition, spectral global and spatial non-local similar low-rank decomposition, norm constraint and so on. the mixed noise (Gaussian noise, impulse noise and dead line) of hyperspectral images which seriously affect the accuracy of anomaly detection are preprocessed. On this basis, the global RX algorithm is used to detect anomalies in the denoised hyperspectral image, and the simulation data are compared with the original real data. The experimental results show that the subspace low-rank decomposition anomaly detection algorithm is better than other existing algorithms in speed and accuracy, which shows the feasibility and superiority of this algorithm.