Edge Extraction Based on Density Analysis in Spectral Space

The density analysis of super dimensional spectral space for edge extraction is presented. On the basis of the first principle component after grouping PCA, the fundamental methods of object-oriented two-times edges determination is proposed. Every edge point called candidate detecting from each first component will perform classification in feature space as vectors. Edge feature points are distributed in the form of lower density hyperellipsoid in spectral space because they have class characteristics like common points of large areas. Real edge points are considered as those in lower density zones of spectral space.