Fuzzy image classification using multiresolution neural networks with applications to remote sensing

Avrithis Yannis, S; Kollias Stefanos, D

dc.contributor.author	Avrithis Yannis, S	en
dc.contributor.author	Kollias Stefanos, D	en
dc.date.accessioned	2014-03-01T02:41:28Z
dc.date.available	2014-03-01T02:41:28Z
dc.date.issued	1997	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30473
dc.subject	Computational Complexity	en
dc.subject	Hierarchical Neural Networks	en
dc.subject	Image Analysis	en
dc.subject	Image Classification	en
dc.subject	Image Representation	en
dc.subject	Land Cover Mapping	en
dc.subject	Remote Sensing	en
dc.subject	Remote Sensing Data	en
dc.subject	Supervised Classification	en
dc.subject	Neural Network	en
dc.subject.other	Computational complexity	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Hierarchical systems	en
dc.subject.other	Image analysis	en
dc.subject.other	Neural networks	en
dc.subject.other	Remote sensing	en
dc.subject.other	Fuzzy image classification	en
dc.subject.other	Object recognition	en
dc.title	Fuzzy image classification using multiresolution neural networks with applications to remote sensing	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICDSP.1997.628055	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICDSP.1997.628055	en
heal.publicationDate	1997	en
heal.abstract	Recent progress in supervised image classification research, has demonstrated the potential usefulness of incorporating fuzziness in the training, allocation and testing stages of several classification techniques. In this paper a multiresolution neural network approach to supervised classification is presented, exploiting the inherent fuzziness of such techniques in order to perform classification at different resolution levels and gain in computational complexity. In particular, multiresolution image analysis is carried out and hierarchical neural networks are used as an efficient architecture for classification of the derived multiresolution image representations. A new scheme is then introduced for transferring classification results to higher resolutions based on the fuzziness of the results of lower resolutions, resulting in faster implementation. Experimental results on land cover mapping applications from remotely sensed data illustrate significant improvements in classification speed without deterioration of representation accuracy.	en
heal.publisher	IEEE, Piscataway, NJ, United States	en
heal.journalName	International Conference on Digital Signal Processing, DSP	en
dc.identifier.doi	10.1109/ICDSP.1997.628055	en
dc.identifier.volume	1	en
dc.identifier.spage	261	en
dc.identifier.epage	264	en