Comparative Analysis of SVM and CNN for Hyperspectral Image Classification

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Authors

Md Laraib Salam, Raghvendra Sahai Saxena

Abstract

This paper presents a comparative analysis of tra- ditional machine learning methods and Convolutional Neural Networks (CNNs) for hyperspectral image classification. Utilizing the Indian Pines dataset, we explore the efficacy of Principal Component Analysis (PCA) combined with a Support Vector Machine (SVM) classifier against a deep learning approach involving CNNs. Our methodology includes dimensionality re- duction via PCA, followed by SVM classification, and the design of a tailored CNN model for hyperspectral data. Performance metrics like accuracy, supported with confusion matrices and classification maps, are employed to evaluate and compare the models. Results indicate that CNNs, with their ability to capture spatial and spectral information, outperform traditional methods in classification accuracy and robustness.

DOI

https://doi.org/10.31223/X5811M

Subjects

Computer Engineering, Computer Sciences, Other Computer Sciences, Physical Sciences and Mathematics

Keywords

This paper presents a comparative analysis of tra- ditional machine learning methods and Convolutional Neural Networks (CNNs) for hyperspectral image classification. Utilizing the Indian Pines dataset, we explore the efficacy of Principal Component Analysis (PCA) combined with a Support Vector Machine (SVM) classifier against a deep learning approach involving CNNs. Our methodology includes dimensi, followed by SVM classification, and the design of a tailored CNN model for hyperspectral data. Performance metrics like accuracy, supported with confusion matrices and classification maps, are employed to evaluate and compare the models. Results indicate that CNNs, with their ability to capture spatial and spectral information, outperform traditional methods in classification accuracy and robustness., Hyperspectral Imaging, Convolutional Neural Network (CNN), Principal Component Analysis (PCA), Support Vector Machine (SVM) classifier

Dates

Published: 2024-08-13 14:02

Last Updated: 2024-08-14 16:42

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License

CC BY Attribution 4.0 International

Additional Metadata

Data Availability (Reason not available):
https://purr.purdue.edu/publications/1947/1