Superresolution border segmentation and measurement in remote sensing images.

CIPOLLETTI, M.C.; DELRIEUX, C.; PERILLO, G.M.E.; PICCOLO M. C.

COMPUTERS & GEOSCIENCES

PERGAMON-ELSEVIER SCIENCE LTD

Lugar: Amsterdam; Año: 2012 vol. 40 p. 87 - 96

0098-3004

Segmentation and measurement of linear characteristics in remote sensing imagery are among the first stages in several geomorphologic studies, including the length estimation of geographic features such as perimeters,coastallines,andborders.However,unlike area measurement algorithms, widely used methods for perimeter estimation indigital images have high systematic errors. No precision improvement can be achieved with finer spatial resolution images because of the inherent geometrical inaccuracies they commit. In this work, a superresolution border segmentation and measurement algorithm is presented. The method is based on minimum distance segmentation over the initial image,followed by contour tracking using a superresolution enhancement of the marching squares algorithm. Thorough testing with synthetic and validated field images shows that this algorithm out performs traditional border measuring methods, regardless of the image resolution or theorientation, size, and shape of the object to be analyzed.