Journal of Spectral Imaging,   Volume 11   Article ID a6   (2022)

Peer reviewed Paper

A comparison of common factor-based methods for hyperspectral image exploration: principal components analysis, maximum autocorrelation factors (MAF), minimum noise factors (MNF) and maximum difference factors (MDF)

  • Neal B. Gallagher  
 Corresponding Author
Eigenvector Research, Inc., 300 Bella Strada Lane, Manson, WA 98831, USA
[email protected]
 https://orcid.org/0000-0003-3446-2820
 Search for papers by this author

Principal components analysis (PCA), maximum autocorrelation factors (MAF), minimum noise factors (MNF) and maximum difference factors (MDF) models are common factor-based models used for analysis of hyperspectral images. The models can be posed as maximisation problems that result in a symmetric eigenvalue problem (SEP) for each model. The SEPs allow a simple theoretical comparison of the models using a PCA metaphor with MAF, MNF and MDF describable as weighted PCA models. The examples show that the different methods captured different signals in the images that can be examined individually or combined synergistically allowing for additional modelling and extended visualisation. MDF is a factor-based edge detection model that not only allows for additional visualisation but the opportunity to identify and exclude (or highlight) edge signal in the images. An example shows that models can also be used synergistically for finding and elucidating anomalies. In the example, MDF showed the highest sensitivity of the models studied for anomaly detection.

Keywords: maximum autocorrelation factors, minimum noise factors, maximum difference factors

Metrics

Downloads:

238

Abstract Views:

1,043