Multiscale filter-based hyperspectral image classification with PCA and SVM

Author:

Chen Guang Yi1

Affiliation:

1. Department of Computer Science and Software Engineering , Concordia University , Montreal , QC, Canada

Abstract

Abstract Hyperspectral imagery can offer images with high spectral resolution and provide a unique ability to distinguish the subtle spectral signatures of different land covers. In this paper, we develop a new algorithm for hyperspectral image classification by using principal component analysis (PCA) and support vector machines (SVM). We use PCA to reduce the dimensionality of an HSI data cube, and then perform spatial convolution with three different filters on the PCA output cube. We feed all three convolved output cubes to SVM to classify every pixel. Finally, we perform fusion on the three output maps to determine the final classification map. We conduct experiments on three widely used hyperspectral image data cubes (ie indian pines, pavia university, and salinas). Our method can improve the classification accuracy significantly when compared to several existing methods. Our novel method is relatively fast in term of CPU computational time as well.

Publisher

Walter de Gruyter GmbH

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Classification of Land Patterns from Indian Pines Dataset Using ResCNN;2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE);2023-11-01

2. Principal Component Analysis in Image Classification: A review;2023 Advances in Science and Engineering Technology International Conferences (ASET);2023-02-20

3. A hybrid classification method with dual-channel CNN and KELM for hyperspectral remote sensing images;International Journal of Remote Sensing;2023-01-02

4. Image classification of hyperspectral remote sensing using semi-supervised learning algorithm;Mathematical Biosciences and Engineering;2023

5. Attention Mechanism and Depthwise Separable Convolution Aided 3DCNN for Hyperspectral Remote Sensing Image Classification;Remote Sensing;2022-05-05

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