A support vector data description scheme for hyperspectral target detection using first-order Markov modeling
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Spectral variability remains a challenging problem for target detection in hyperspectral (HS) imagery. In this paper, we have applied the kernel-based support vector data description (SVDD) to perform full-pixel target detection. In target detection scenarios, we do not have a collection of samples characterizing the target class; we are typically given a pure target signature that is obtained from a spectral library. In our work, we use the pure target signature and first-order Markov theory to generate N samples to model the spectral variability of the target class. We vary the value of N and observe its effect to determine a value of N that provides acceptable detection performance. We have inserted target signatures into an urban HS scene with varying levels of spectral variability to explore the performance of the proposed SVDD target detection scheme in these scenarios. The proposed approach makes no assumptions regarding the underlying distribution of the scene data as do traditional stochastic detectors such as the adaptive matched filter (AMF). Detection results in the form of confusion matrices and receiver-operating- characteristic (ROC) curves demonstrate that the proposed SVDD-based scheme is highly accurate and yields higher true positive rates (TPR) and lower false positive rates (FPR) than the AMF. © 2010 SPIE.
author list (cited authors)
Sakla, W. A., Sakla, A. A., Chan, A., & Ji, J.