Object classification using discriminating features derived from higher-order spectra of hyperspectral imagery

This paper describes a novel approach for the detection and classification of man-made objects using discriminating features derived from higher-order spectra (HOS), defined in terms of higher-order moments of hyperspectral-signals. Many existing hyperspectral analysis techniques are based on linearity assumptions. However, recent research suggests that significant nonlinearity arises due to multipath scatter, as well as spatially varying atmospheric water vapor concentrations. Higher-order spectra characterize subtle complex nonlinear dependencies in spectral phenomenology of objects in hyperspectral data and are insensitive to additive Gaussian noise. By exploiting these HOS properties, we have devised a robust method for classifying man-made objects from hyerspectral signatures despite the presence of strong background noise, confusers with spectrally similar signatures and variable signal-to-noise ratios. We tested classification performance hyperspectral imagery collected from several different sensor platforms and compared our algorithm with conventional classifiers based on linear models. Our experimental results demonstrate that our HOS algorithm produces significant reductions in false alarms. Furthermore, when HOS-based features were combined with standard features derived from spectral properties, the overall classification accuracy is substantially improved.