Dimension reduction methods applied to coastline extraction on hyperspectral imagery

Ozan Arslan*, Özer Akyürek, Şinasi Kaya, Dursun Z. Şeker

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

In this study, dimensionality reduction (DR) methods on a hyperspectral dataset to explore the influence on the process of extraction of coastlines were examined and performance of different DR algorithms on the detection of coastline in Bosphorus, Istanbul was investigated. Among these methods, principal component (PC) analysis, maximum noise fraction and independent component (IC) analysis were used in the experiments with the aim of comparing. The study was carried out using these well-known DR techniques on a real hyperspectral image, an Hyperion data set with 161 bands, in the course of the experiments. Three different classifiers (i.e. ML, SVM and neural network) were used for the classification of dimensionally reduced and original images to detect coastline in the region. The DR results were evaluated quantitatively and visually in order to determine the reduced dimensions of the image subsets. Findings show that there is no significant influence of using DR methods on the dataset on the detection of coastline.

Original languageEnglish
Pages (from-to)376-390
Number of pages15
JournalGeocarto International
Volume35
Issue number4
DOIs
Publication statusPublished - 11 Mar 2020

Bibliographical note

Publisher Copyright:
© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.

Keywords

  • coastline
  • dimension reduction
  • Hyperspectral imagery
  • image analysis
  • remote sensing

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