Oscillatory correlation for scene segmentation of hyperspectral imagery for geospatial analysis

Abstract

A neurodynamical approach to scene segmentation of hyperspectral imagery is investigated based on oscillatory correlation theory. A network of relaxation oscillators, which is based on the Locally Excitatory Globally Inhibitory Oscillator Network (LEGION), is extended to process multiband data and it is implemented to perform unsupervised scene segmentation using both spatial and spectral information. The nonlinear dynamical network is capable of achieving segmentation of objects in a scene by the synchronization of oscillators that receive local excitatory inputs from a collection of local neighbors and desynchronization between oscillators corresponding to different objects. The original LEGION model was designed for single-band imagery. The proposed multiband version of LEGION is implemented such that the connections in the oscillator network receive the spectral pixel vectors in the hyperspectral data as excitatory inputs. Euclidean distances between spectra in local neighborhoods are used as the measure of closeness in the network. The ability of the proposed approach to perform natural and urban scene segmentation for geospatial analysis is assessed. Our approach is tested on two hyperspectral datasets with notably different sensor properties and scene content.