Clustering Hyperspectral Imagery for Improved Adaptive Matched Filter Performance
This paper offers improvements to adaptive matched filter (AMF) performance by addressing correlation and non-homogeneity problems inherent to hyperspectral imagery (HSI). The estimation of the mean vector and covariance matrix of the background should be calculated using “target-free” data. This statement reflects the difficulty that including target data in estimates of the mean vector and covariance matrix of the background could entail. This data could act as statistical outliers and severely contaminate the estimators. This fact serves as the impetus for a 2-stage process: First, attempt to remove the target data from the background by way of the employment of anomaly detectors. Next, with remaining data being relatively “target-free” the way is cleared for signature matching. Relative to the first stage, we were able to test seven different anomaly detectors, some of which are designed specifically to deal with the spatial correlation of HSI data and/or the presence of anomalous pixels in local or global mean and covariance estimators. Relative to the second stage, we investigated the use of cluster analytic methods to boost AMF performance. The research shows that accounting for spatial correlation effects in the detector yields nearly “target-free” data for use in an AMF that is greatly benefitted through the use of cluster analysis methods.
Journal of Applied Remote Sensing
J. P. Williams, K. W. Bauer, and M. A. Friend, “Clustering hyperspectral imagery for improved adaptive matched filter performance,” J. Appl. Remote Sens. 7(1), 73547 (2013) [doi:10.1117/1.JRS.7.073547]. https://doi.org/10.1117/1.JRS.7.073547