Vector Attribute Profiles for Hyperspectral Image Classification

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Date

2016

Journal Title

Journal ISSN

Volume Title

Publisher

Ieee-inst Electrical Electronics Engineers inc

Abstract

Morphological attribute profiles are among the most prominent spectral-spatial pixel description methods. They are efficient, effective, and highly customizable multiscale tools based on hierarchical representations of a scalar input image. Their application to multivariate images in general and hyperspectral images in particular has been so far conducted using the marginal strategy, i.e., by processing each image band (eventually obtained through a dimension reduction technique) independently. In this paper, we investigate the alternative vector strategy, which consists in processing the available image bands simultaneously. The vector strategy is based on a vector-ordering relation that leads to the computation of a single max and min tree per hyperspectral data set, from which attribute profiles can then be computed as usual. We explore known vector-ordering relations for constructing such max trees and, subsequently, vector attribute profiles and introduce a combination of marginal and vector strategies. We provide an experimental comparison of these approaches in the context of hyperspectral classification with common data sets, where the proposed approach outperforms the widely used marginal strategy.

Description

Dalla Mura, Mauro/0000-0002-9656-9087; Aptoula, Erchan/0000-0001-6168-2883; Lefevre, Sebastien/0000-0002-2384-8202

Keywords

Hyperspectral images, morphological attribute profiles, multivariate morphology, vector ordering

Turkish CoHE Thesis Center URL

WoS Q

Q1

Scopus Q

Q1

Source

Volume

54

Issue

6

Start Page

3208

End Page

3220