Title :
An Adaptive Threshold Method for Hyperspectral Target Detection
Author :
Broadwater, Joshua ; Chellappa, Rama
Author_Institution :
Center for Autom. Res., Maryland Univ., College Park, MD
Abstract :
In this paper, we present a new approach to automatically determine a detector threshold. This research problem is especially important in hyperspectral target detection as targets are typically very similar to the background. While a number of methods exist to determine the threshold, these methods require either large amounts of data or make simplifying assumptions about the background distribution. We use a method called inverse blind importance sampling which requires few samples and makes no a-priori assumptions about the background statistics. Results show the promise of this algorithm to determine thresholds for fixed false alarm densities in hyperspectral detectors
Keywords :
geophysical signal processing; importance sampling; object detection; adaptive threshold method; background statistics; hyperspectral target detection; inverse blind importance sampling; Automation; Closed-form solution; Detectors; Educational institutions; Gaussian distribution; Hyperspectral imaging; Monte Carlo methods; Object detection; Probability distribution; Statistical distributions;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on
Conference_Location :
Toulouse
Print_ISBN :
1-4244-0469-X
DOI :
10.1109/ICASSP.2006.1661497