Supervised Distance-Based Feature Selection for Hyperspectral Target Detection

Supervised Distance-Based Feature Selection for Hyperspectral Target Detection

Blog Article

Feature/band selection (FS/BS) for target detection (TD) attempts to select features/bands that increase the discrimination between the target and the image background.Moreover, TD usually suffers from background interference.Therefore, bands that help detectors to effectively suppress 6-0 igora vibrance the background and magnify the target signal are considered to be more useful.In this regard, three supervised distance-based filter FS methods are proposed in this paper.The first method is based on the TD concept.

It uses the image autocorrelation matrix and the target signature in the detection space (DS) for FS.Features that increase the first-norm distance between the target energy and the mean energy of the background in DS are selected as optimal.The other two methods use background modeling via image clustering.The cluster mean spectra, along with the target spectrum, are then transferred into DS.Orthogonal subspace projection distance (OSPD) and first-norm distance (FND) are used as two FS criteria to select optimal features.

Two bostik universal primer pro datasets, HyMap RIT and SIM.GA, are used for the experiments.Several measures, i.e., true positives (TPs), false alarms (FAs), target detection accuracy (TDA), total negative score (TNS), and the receiver operating characteristics (ROC) area under the curve (AUC) are employed to evaluate the proposed methods and to investigate the impact of FS on the TD performance.

The experimental results show that our proposed FS methods, as compared with five existing FS methods, have improving impacts on common target detectors and help them to yield better results.