BibliographicReference 131527

BibliographicReference record

Type: Bibliographic resource
Type of document: Book chapters
Type of document: Conference paper
BibLvlCode: AM
Title: Classifying hyperspectral airborne imagery for vegetation survey along coastlines
Abstract: This paper studies the potential of airborne hyperspectral imagery for classifying vegetation along the Belgian coastlines. Here, the aim is to build vegetation maps using automatic classification. Besides a general linear multiclass classifier (Linear Discriminant Analysis), several strategies for combining binary classifiers are proposed: one based on a hierarchical decision tree, one based on the Hamming distance between the codewords obtained by binary classifiers and one based on the coupling of posterior probabilities. In addition, a new procedure is proposed for spatial classification smoothing. This procedure takes into account spatial information by letting the decision for classification of a pixel depend on the classification probabilities of neighboring pixels. This is shown to render smoother classification images.
Bibliographic citation: Kempeneers, P.; Deronde, B.; Bertels, L.; Debruyn, W.; De Backer, S.; Scheunders, P. (2004). Classifying hyperspectral airborne imagery for vegetation survey along coastlines, in: Proceedings of Geoscience and Remote Sensing Symposium, 20-24 september 2004. Anchorage, Alaska. Volume 2. pp. 1475-1478
Topic: Marine
Access rights: open access
Is accessible for free: true

term: Airborne sensing (term code: 246 - defined in term set: ASFA Thesaurus List); Geosensing (term code: 3665 - defined in term set: ASFA Thesaurus List); Hyperspectral imaging (term code: 67697 - defined in term set: CSA Technology Research Database Master Thesaurus); Remote sensing (term code: 94323 - defined in term set: Transportation Research Thesaurus)