An end-member based ordering relation for the morphological description of hyperspectral images

No Thumbnail Available

Date

2014

Journal Title

Journal ISSN

Volume Title

Publisher

Institute of Electrical and Electronics Engineers Inc.

Open Access Color

OpenAIRE Downloads

OpenAIRE Views

Research Projects

Organizational Units

Journal Issue

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

Fields of Science

Citation

5

WoS Q

Scopus Q

Source

2014 IEEE International Conference on Image Processing, ICIP 2014

Volume

Issue

Start Page

5097

End Page

5101