Dimensionality Reduction for Hyperspectral Image Classification

• URL: http://arxiv.org/abs/2609.10334v1
• Date: Wed, 09 Sep 2026 15:32:52 GMT
• Title: Dimensionality Reduction for Hyperspectral Image Classification
• Abstract summary: It deals with two fundamental aspects: dimensionality reduction of data and the selection of appropriate supervised classification techniques.<n>We examine two commonly used methods: Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA)<n>We compare the performance of three methods: K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Random Forest (RF) using real hyperspectral data.
• Score: 3.7015437844242904
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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