Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/9634
Title: Tensor-Cuts: A simultaneous multi-type feature extractor and classifier and its application to road extraction from satellite images
Authors: Poullis, Charalambos 
Keywords: Feature classification
Feature extraction
Graph-Cuts
Road extraction
Tensor
Issue Date: 1-Jan-2014
Publisher: Elsevier
Source: ISPRS Journal of Photogrammetry and Remote Sensing Volume 95, September 2014, Pages 93-108
Abstract: Many different algorithms have been proposed for the extraction of features with a range of applications. In this work, we present Tensor-Cuts: a novel framework for feature extraction and classification from images which results in the simultaneous extraction and classification of multiple feature types (surfaces, curves and joints). The proposed framework combines the strengths of tensor encoding, feature extraction using Gabor Jets, global optimization using Graph-Cuts, and is unsupervised and requires no thresholds. We present the application of the proposed framework in the context of road extraction from satellite images, since its characteristics makes it an ideal candidate for use in remote sensing applications where the input data varies widely. We have extensively tested the proposed framework and present the results of its application to road extraction from satellite images.
URI: http://ktisis.cut.ac.cy/handle/10488/9634
ISSN: 09242716
Rights: © 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).
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