On the effect of synthetic morphological feature vectors on hyperspectral image classification performance
No Thumbnail Available
Date
2015
Journal Title
Journal ISSN
Volume Title
Publisher
Ieee
Open Access Color
OpenAIRE Downloads
OpenAIRE Views
Abstract
This paper studies the effect of synthetic feature vectors on the classification performance of hyperspectral remote sensing images. As feature vectors, it has been chosen to employ morphological attribute profiles, that have proven themselves in this field. At this early stage of our work, the relatively simple Bootstrapping algorithm has been used for synthetic feature vector generation. Based on experiments conducted on multiple hyperspectral datasets, it has been observed that synthetic feature vectors contribute considerably to classification performance in the case of limited training dataset sizes.
Description
Aptoula, Erchan/0000-0001-6168-2883; Yanikoglu, Berrin/0000-0001-7403-7592; Davari, Amirabbas/0000-0001-6672-283X
Keywords
remote sensing, hyperspectral image, extended morphological attribute profile, bootstrap, resampling, classification
Turkish CoHE Thesis Center URL
Fields of Science
Citation
4
WoS Q
Scopus Q
Source
23nd Signal Processing and Communications Applications Conference (SIU) -- MAY 16-19, 2015 -- Inonu Univ, Malatya, TURKEY
Volume
Issue
Start Page
653
End Page
656