Marching cubes: A high resolution 3D surface construction algorithm
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Sub-pixel distance maps and weighted distance transforms
Journal of Mathematical Imaging and Vision - Special issue on topology and geometry in computer vision
Introductory Digital Image Processing: A Remote Sensing Perspective
Introductory Digital Image Processing: A Remote Sensing Perspective
The asymptotic decider: resolving the ambiguity in marching cubes
VIS '91 Proceedings of the 2nd conference on Visualization '91
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
Remote sensing and GIS technology in the Global Land Ice Measurements from Space (GLIMS) Project
Computers & Geosciences
Local Properties of Binary Images in Two Dimensions
IEEE Transactions on Computers
Sub-pixel edge detection based on an improved moment
Image and Vision Computing
A graph-based framework for sub-pixel image segmentation
Theoretical Computer Science
An optimal polygonal boundary encoding scheme in the rate distortion sense
IEEE Transactions on Image Processing
Border extrapolation using fractal attributes in remote sensing images
Computers & Geosciences
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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, coastal lines, and borders. However, unlike area measurement algorithms, widely used methods for perimeter estimation in digital 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 outperforms traditional border measuring methods, regardless of the image resolution or the orientation, size, and shape of the object to be analyzed.