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.

Distributed adaptive framework for multispectral/hyperspectral imagery and three-dimensional point cloud fusion

“…14(a). For example, the SEM method for HSI segmentation could be replaced by alternative unsupervised approaches, such as one of the newer spectral segmentation methods mentioned earlier, [6][7][8][9][10][11][12] older methods in Ref. 13, or the spectral-spatial methods mentioned earlier [16][17][18][19][20][21][22][23][24] Furthermore, PCD data also provide the opportunity to use scene geometry and solar illumination conditions to make radiometric corrections to improve classification results by mitigating shadows and illumination variations.…”

“…In the first process, PCD is segmented based on the multidimensional meanshift segmentation algorithm, where the results are clustered based on the geometry of points in the point cloud. In the second process, any of a number of unsupervised spectralonly 1,[7][8][9][10][11][12] or spectral-spatial methods [16][17][18][19][20][21][22][23][24] could be applied to HSI data to segment the data into a classification map of material compositions based on spectral information. We select the SEM as our choice algorithm to spectrally segment the data, because it is theoretically rigorous and more sophisticated than older segmentation methods, such as k-means 13 or ISODATA, 13 and because it can also especially be applied to MSI.…”

Residential Area Recognition Using Oscillatory Correlation Segmentation of Hyperspectral Imagery

“…And it has been successfully applied to segmenting grayscale images, medical images, and aerial images [3]. However, previous experiments showed that troubles will be encountered when using original LEGION to segment HSI [4], especially in synchronizing narrow-shaped objects in high variance associated with bright intensity values. It's hard for lateral potential obtain a leader in small narrow-shaped orientated area.…”

“…S is the total coupling from the adjacent active neighbors of oscillator : (4) where W ik defines the dynamic connection weight from oscillator k to i and N(i) represents a set of oscillators that comprises the neighborhood of i For image segmentation, the LEGION network generally has 2-D architecture, as illustrated in Fig. 1, where each oscillator corresponds to a pixel in the given image and is connected to its eight nearest neighbors except for the boundaries where there is no wrap around.…”