Dimensionality Reduction for Hyperspectral Image Classification

EasyChair Preprint no. 11193

Abstract

This paper addresses the issue of supervised classification in the context of hyperspectral satellite images. It deals with two fundamental aspects: dimensionality reduction of data and the selection of appropriate supervised classification techniques.

Firstly, we delve into dimensionality reduction, a critical step in simplifying the management of hyperspectral data. The reduction aims to decrease complexity in terms of memory and computing time. We examine two commonly used methods: Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).

Subsequently, we explore the selection of the most suitable supervised classification algorithms for hyperspectral images. We compare the performance of three methods: K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Random Forest (RF) using real hyperspectral data. The results highlight that the combination of PCA and RF yields the highest overall accuracy and Kappa coefficient.

Keyphrases: KNN (K-Nearest Neighbors), LDA (Linear Discriminant Analysis), PCA (Principal Component Analysis), RF (Random Forest), SVM (Support Vector Machine)

@Booklet{EasyChair:11193,
  author = {Cherifi Mohamed and Mesloub Ammar and El Korso Mohammed Nabil and Touhami Tayab and Hacine Gharbi Abdennour},
  title = {Dimensionality Reduction for Hyperspectral Image Classification},
  howpublished = {EasyChair Preprint no. 11193},

  year = {EasyChair, 2023}}