Extending classification approaches to hyperspectral object detection

Abstract

This study adapts a variety of multi-spectral image classification techniques to generate supervised object detection algorithms for hyperspectral imagery, and compares and quantitatively tests them against the Adaptive Cosine Estimator (ACE) and the standard, matched filter (MF). A new search algorithm, Regularized Maximum Likelihood Clustering (RMLC), uses only pixels for the covariance matrix (CV) computation associated with the object after "regularizing" the matrix to avoid singularities and mitigate statistical degradation due to undersampling for small objects. The searches are applied to both visible/near IR and short wave IR data collected from forested areas. This study tests the detection sensitivity by using object signatures and CVs taken directly from the scene and from temporally transformed signatures and object CVs. This study adds simple, high performing algorithms to the small object search arsenal.