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primary topic: 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
type: AcademicArticle
type: Article
abstract: 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 precision agriculture to environmental monitoring. However, extracting insights from complex data requires sophisticated analytical approaches. Our research dives into the performance comparison of two popular machine learning approaches: the support vector machine (SVM) and the more recent deep learning-based stacked autoencoder (SAE). We wanted to understand which approach works better under different real-world conditions that researchers and practitioners face. Through extensive experiments across five diverse public hyperspectral datasets, we discovered that the choice between these models is not straightforward, it depends significantly on your specific circumstances. When labeled data are scarce, which is a common challenge in remote sensing, SVM proves more reliable and efficient. Conversely, when abundant training data are available, SAE demonstrates impressive capabilities in learning complex patterns. One interesting finding was how active learning as a technique that intelligently selects the most informative samples for labeling, improved SAE’s performance on medium-sized datasets, potentially offering a practical solution to the data scarcity problem. The proposed approaches showed vulnerability to noise, highlighting the importance of preprocessing steps in real-world applications. Although SVM generally requires less computational resources, SAE’s potential to handle large and complex datasets makes it an attractive option when the appropriate computing infrastructure is available. The model training also achieved high accuracy, compared to other models published in the literature. The results achieved provide a practical path for researchers and practitione…
date: 2025-06
volume: 18
is part of: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository
has document: Performance Evaluation of Support Vector Machine and Stacked Autoencoder for Hyperspectral Image Analysis (Texto)
has document: Performance Evaluation of Support Vector Machine and Stacked Autoencoder for Hyperspectral Image Analysis (Otro)
subject: Engineering

Performance Evaluation of Support Vector Machine and Stacked Autoencoder for Hyperspectral Image Analysis (Texto)
type: Document
has file: Performance_Evaluation_of_Support_Vector_Machine_and_Stacked_Autoencoder_for_Hyperspectral_Image_Analysis.pdf
has part: Performance_Evaluation_of_Support_Vector_Machine_and_Stacked_Autoencoder_for_Hyperspectral_Image_Analysis.pdf

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
type: Collection
issn: 19391404

Isabel De la Torre Díez
type: Person
given name: Isabel
family name: De la Torre Díez

Irene Delgado Noya
type: Person
given name: Irene
family name: Delgado Noya

Brahim Jabir
type: Person
given name: Brahim
family name: Jabir

Bendaoud Nadif
type: Person
given name: Bendaoud
family name: Nadif

Helena Garay
type: Person
given name: Helena
family name: Garay

Performance Evaluation of Support Vector Machine and Stacked Autoencoder for Hyperspectral Image Analysis (Otro)
type: Document
is version of: Performance Evaluation of Support Vector Machine and Stacked Autoencoder for Hyperspectral Image Analysis (Texto)
is volatile version of: Performance Evaluation of Support Vector Machine and Stacked Autoencoder for Hyperspectral Image Analysis (Texto)
is index codes version of: Performance Evaluation of Support Vector Machine and Stacked Autoencoder for Hyperspectral Image Analysis (Texto)
has file: indexcodes.txt
has part: indexcodes.txt

HTML Summary of #17866 Performance Evaluation of Support Vector Machine and Stacked Autoencoder for Hyperspectral Image Analysis
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primary topic: Performance Evaluation of Support Vector Machine and Stacked Autoencoder for Hyperspectral Image Analysis

Engineering
type: Concept
preferred label: Ingeniería @es