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Proceedings Paper

Extraction of built-up areas from remote sensing imagery using one-class classification
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Paper Abstract

Mapping of built-up areas were always a main concern to researchers in the field of remote sensing. Thus, several techniques have been proposed to saving technicians from digitizing hundreds of areas by hand. Multiclass classifiers exhibit a very promising performance in terms of classification accuracy. However, they require that all classes in the study area to be labeled. In many applications, users may only be interested in a specific land class. This referred to as one-class classification (OC) problem. In this paper, we compare a Binary Support Vector Machine (BSVM) classifier, with two OC classifiers, OC SVM (OCSVM), and Presence and Background Learning (PBL) framework for the extracting built-up areas from Gaofen-2 and Aster satellites imagery. The obtained classification accuracies show that PBL provides competitive extraction results due to the fact that PBL is a positive-unlabeled method based on neural network in which large amounts of available unlabeled samples is incorporated into the training phase, allowing the classifier to model the built-up class more effectively.

Paper Details

Date Published: 7 October 2019
PDF: 7 pages
Proc. SPIE 11155, Image and Signal Processing for Remote Sensing XXV, 1115521 (7 October 2019); doi: 10.1117/12.2535598
Show Author Affiliations
Khelifa Djerriri, Ctr. National des Techniques Spatiales (Algeria)
Zakaria Benyelles, Ctr. National des Techniques Spatiales (Algeria)
Dalila Attaf, Ctr. National des Techniques Spatiales (Algeria)
Rabia Sarah Cheriguene, Ctr. National des Techniques Spatiales (Algeria)

Published in SPIE Proceedings Vol. 11155:
Image and Signal Processing for Remote Sensing XXV
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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