Semi-Automatic Linear Feature Extraction by Dynamic Programming and LSB-Snakes

Abstract

This paper deals with semi-automatic linear feature extraction from digital images for GIS data capture, where the identification task is pe$ormed manually on a single image, while a special automatic digital module performs the high precision feature tracking in two-dimensional (2-0) image space or even three-dimensional (3-0) object space. A human operator identifies the object from an on-screen display of a digital image, selects the particular class this object belongs to, and provides a very few coarsely distributed seed points. subseq;ently, with th;?sk seed as an approximation of the ~osition and sham the linear feature will be extracted automatically by either a dynamic programming approach or by LSB-S~~~~S [Least-Squares E-spline Snakes). With dynamic programming, the optimization problem is set up as a discrete multistage decision process and is solved by a timedelayed algorithm. It ensures global optimality, is numerically stable, and allows for hard constraints to be enforced on the solution. In the least-squares approach, we combine three types of observation equations, one radiometric, formulating the matching of a generic object model with image data, and two that express the internal geometric constraints of a curve and the location of operator-given seed points. The solution is obtained by solving a pair of independent normal equations to estimate the parameters of the spline curve. Both techniques can be used in a monoplotting mode, which combines one image with its underlying DTM. The LSB-S~~~~S approach is also implemented in a multi-image mode, which uses multiple images simultaneously and provides for a robust and mathematically sound full 3D approach. These techniques are not restricted to aerial images. They can be applied to satellite and close-range images as well. The issues related to the mathematical modeling of the proposed methods are discussed and experimental results are shown in this paper too.