Dimensionality reduction techniques with HydraNet framework for HSI classification

Mohammed Q. Alkhatib, Mina Al-Saad, Nour Aburaed, Saeed Al Mansoori, Hussain Al Ahmad

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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Abstract

Hyperspectral Imagery (HSI) classification is an important research area in remote sensing community due to its high efficiency in accurately analyzing ground features by assigning a class label to each pixel. This paper explores the use of Band Subset selection (BSS) methods as Dimensionality Reduction (DR) pre-processing stage for HSI classification, and compares them to Principal Component Analysis (PCA) approach. BSS is the problem of selecting the most independent bands in HSI cube. Classification is then performed using a proposed multi-branch HydraNet model that combines 1D, 2D, and 3D convolution. HydraNet is trained and tested using the benchmark Pavia University dataset, and the results are evaluated using Kappa and Overall Accuracy. Experimental results show positive indications of the network's performance, especially when compared to other state-of-the-art CNN networks.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Image Processing, ICIP 2022 - Proceedings
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages3151-3155
Number of pages5
ISBN (Electronic)9781665496209
ISBN (Print)9781665496216
DOIs
Publication statusPublished - 18 Oct 2022
Event29th IEEE International Conference on Image Processing, ICIP 2022 - Bordeaux, France
Duration: 16 Oct 202219 Oct 2022

Conference

Conference29th IEEE International Conference on Image Processing, ICIP 2022
Country/TerritoryFrance
CityBordeaux
Period16/10/2219/10/22

Keywords

  • band subset selection
  • dimensionality reduction
  • HSI classification
  • HydraNet

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