Performance Evaluation of Support Vector Machine and Stacked Autoencoder for Hyperspectral Image Analysis

Performance Evaluation of Support Vector Machine and Stacked Autoencoder for Hyperspectral Image Analysis

In the world of remote sensing, hyperspectral imaging has emerged as a powerful tool that captures incredibly detailed information about our environment. These images contain hundreds of spectral bands that reveal what the human eye cannot see, making them invaluable for applications ranging from pr...

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Bibliographic Details
Main Authors: Brahim Jabir, Bendaoud Nadif, Isabel De la Torre Diez, Helena Garay, Irene Delgado Noya
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Active learning
deep learning
hyperspectral data classification
remote sensing
stacked autoencoder (SAE)
support vector machine (SVM)
Online Access:https://ieeexplore.ieee.org/document/11039027/
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