An end-member based ordering relation for the morphological description of hyperspectral images
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Date
2014
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Institute of Electrical and Electronics Engineers Inc.
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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.
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Keywords
classification, end-members, hyperspectral images, Mathematical morphology, vector ordering
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5
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Source
2014 IEEE International Conference on Image Processing, ICIP 2014
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Start Page
5097
End Page
5101