J
J.A. Palmason
Researcher at University of Iceland
Publications - 8
Citations - 1730
J.A. Palmason is an academic researcher from University of Iceland. The author has contributed to research in topics: Hyperspectral imaging & Structuring element. The author has an hindex of 6, co-authored 8 publications receiving 1516 citations.
Papers
More filters
Journal ArticleDOI
Classification of hyperspectral data from urban areas based on extended morphological profiles
TL;DR: A method based on mathematical morphology for preprocessing of the hyperspectral data is proposed, using opening and closing morphological transforms to isolate bright and dark structures in images, where bright/dark means brighter/darker than the surrounding features in the images.
Journal ArticleDOI
Exploiting spectral and spatial information in hyperspectral urban data with high resolution
TL;DR: New methods for classification of hyperspectral remote sensing data are investigated, with the primary focus on multiple classifications and spatial analysis to improve mapping accuracy in urban areas.
Proceedings ArticleDOI
Classification of hyperspectral data from urban areas using morphological preprocessing and independent component analysis
TL;DR: This paper investigates the use of independent components instead of principal components in extended Morphological profiles, i.e., selected independent components are used as base images for an extended morphological profile and used as inputs to a neural network classifier.
Proceedings ArticleDOI
Morphological transformations and feature extraction of urban data with high spectral and spatial resolution
TL;DR: The morphological approach is applied in experiments on high resolution DAIS remote sensing data from an urban area and it is observed that classification on reduced features gives higher accuracies than in the original feature space.
Proceedings ArticleDOI
Fusion of Morphological and Spectral Information for Classification of Hyperspectal Urban Remote Sensing Data
TL;DR: An extension is proposed in this paper in order to overcome the problems with the extended morphological profile approach and achieve significant improvements in terms of accuracies when compared to results of approaches based on the use of morphological profiles based on PCs only and conventional statistical approaches.