Applying cellular automata to hyperspectral edge detection

This paper proposes the concept of using cellular automata (CAs) and adapted edge detection algorithms for edge detection in hyperspectral images. The approach consists of an edge detection CA and a post-processing CA (that implements morphological operations for denoising the edges). The CA approach is generalized in that it operates on any three-dimensional data cube, allowing for hyperspectral dimensionality reduction as a pre-processing stage if preferred. The CA approach is designed, implemented, and applied to airborne hyperspectral imagery for qualitative assessment and applied to synthetic imagery, where the precise ground truth of edges are known, for quantitative assessment. Results show the CA method to be very promising for both unsupervised and supervised edge detection in hyperspectral imagery.