A study of combination of autoencoders and boosted Big-Bang crunch theory architectures for Land-Use classification using remotely sensed imagery

A study of combination of autoencoders and boosted Big-Bang crunch theory architectures for Land-Use classification using remotely sensed imagery

Abstract The research introduced a new method for land-use classification by merging deep convolutional neural networks with a modified variant of a metaheuristic optimization technique. The methodology involved utilizing the VGG-19 model for feature extraction, dimensionality reduction, and a stack...

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Bibliographic Details
Main Authors: Qiongbing Xiong, Xuecheng Wu, Cizhen Yu, Hasan Hosseinzadeh
Format: Article
Language:English
Published: Nature Portfolio 2025-05-01
Series:Scientific Reports
Subjects:
Land-use classification
Deep learning
VGG-19
Stacked autoencoder
Metaheuristic
Boosted Big-Bang crunch theory
Online Access:https://doi.org/10.1038/s41598-025-99436-4
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https://doi.org/10.1038/s41598-025-99436-4

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