Dimensionality Reduction for Hyperspectral Image Classification

2026-09-09

AI Summary

本文解决了高光谱图像分类中的维数约简与选择合适监督分类技术的问题，通过比较PCA、LDA及KNN、SVM、RF方法，发现PCA结合RF效果最佳。

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.

Problem

• Hyperspectral Image
• Dimensionality Reduction
• Supervised Classification

Innovation

• Dimensionality Reduction
• Hyperspectral Images
• PCA
• Random Forest
• Supervised Classification