Automating knowledge acquisition for aerial image interpretation
Computer Vision, Graphics, and Image Processing
Using Dynamic Programming for Solving Variational Problems in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast algorithm for active contours and curvature estimation
CVGIP: Image Understanding
Finding road seeds in aerial images
CVGIP: Image Understanding
A Bayesian Approach to Dynamic Contours Through Stochastic Sampling and Simulated Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Active Testing Model for Tracking Roads in Satellite Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
New Prospects in Line Detection by Dynamic Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
“Brownian strings”: segmenting images with stochastically deformable contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Remote Sensing and Image Interpretation
Remote Sensing and Image Interpretation
State of the art on automatic road extraction for GIS update: a novel classification
Pattern Recognition Letters
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
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The manual production of vector maps from digital imagery can be a time consuming and costly process. Developing tools to automate this task for specific features, such as roads, has become an important research topic. The purpose of this paper was to present a technique for the semi-automatic extraction of multiple pixel width river features appearing in high resolution satellite imagery. This was accomplished using a two stage, multi-resolution procedure. Initial river extraction was performed on low resolution (SPOT multi-spectral, 20 m) imagery. The results from this low resolution extraction were then refined on higher resolution (KFA1000, panchromatic, 5 m) imagery to produce a detailed outline of the channel banks. To perform low resolution extraction a cost surface was generated to represent the combined local evidence of the presence of a river feature. The local evidence of a river was evaluated based on the results of a number of simple operators. Then, with user specified start and end points for the network, rivers were extracted by performing a least cost path search across this surface using the A* algorithm. The low resolution results were transferred to the high resolution imagery as closed contours which provided an estimate of the channel banks. These contours were then fit to the channel banks using the dynamic contours (or snakes) technique.