An efficient edge detection for remote sensing image based on Wold-like decomposition in random field is presented in this paper. In such assumption that the image field is a realization of a 2-D homogeneous random field, image can be decomposed into a sum of two mutually orthogonal, spatially homogeneous components, namely deterministic in the prediction theory sense and purely indeterministic in the prediction theory. The Wold decomposition can be described by "periodicity," "directionality," and "randomness," approximating what is indicated to be the three most important dimensions of human perception. So, the remote sensing images are firstly decomposed into two components: deterministic component and indeterministic component. On the basis of Wold-like decomposition a new approach of low image processing, SUSAN algorithm, is estimated and recommended in the edge detection on the periodicity component, which presents the structural information convenient for detecting edge. Then this paper made some improvement of the approach in edge detection. The experiments show that the results of edge detection through Wold decomposition are better than that of no Wold decomposition. Simultaneously, the Wold texture modal is applicable to a wide variety of texture types, from structural to stochastic texture. And this modal gives a unified, perfect description of texture in natural images.

Date Published: 28 October 2006
PDF: 6 pages
Proc. SPIE 6419, Geoinformatics 2006: Remotely Sensed Data and Information, 641918 (28 October 2006); doi: 10.1117/12.713137

Published in SPIE Proceedings Vol. 6419:
Geoinformatics 2006: Remotely Sensed Data and Information
Liangpei Zhang; Xiaoling Chen, Editor(s)