An end-member based ordering relation for the morphological description of hyperspectral images
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Date
2014
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Abstract
Despite the popularity of mathematical morphology with remote sensing image analysis, its application to hyperspectral data remains problematic. The issue stems from the need to impose a complete lattice structure on the multi-dimensional pixel value space, that requires a vector ordering. In this article, we introduce such a supervised ordering relation, which conversely to its alternatives, has been designed to be image-specific and exploits the spectral purity of pixels. The practical interest of the resulting multivariate morphological operators is validated through classification experiments where it achieves state-of-the-art performance. © 2014 IEEE.
Description
Keywords
classification, end-members, hyperspectral images, Mathematical morphology, vector ordering
Turkish CoHE Thesis Center URL
Citation
5
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N/A
Scopus Q
N/A
Source
2014 IEEE International Conference on Image Processing, ICIP 2014
Volume
Issue
Start Page
5097
End Page
5101