Linear Feature Extraction with Dynamic Programming and Globally Enforced Least Squares Matching

In this paper we present a method for semi-automatic extraction of road networks from SPOT imagery using wavelet-transformed images and cost functions expressing local gray value variations and global continuity constraints. For larger scale images and for various object types, we propose a global approach for semi-automatic object outline detection which uses local gray value variations to precisely identify edge locations. In this new procedure of globally enforced least squares template matching, the mathematical foundation is provided by least squares matching, while global continuity is enforced through the introduction of object-type-dependent shape constraints.