Spectrally Segmented-Enhanced Neural Network for Precise Land Cover Object Classification in Hyperspectral Imagery

The paradigm shift brought by deep learning in land cover object classification in hyperspectral images (HSIs) is undeniable, particularly in addressing the intricate 3D cube structure inherent in HSI data. Leveraging convolutional neural networks (CNNs), despite their architectural constraints, off...

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Bibliografische gegevens

Hoofdauteurs:	Touhid Islam, Rashedul Islam, Palash Uddin, Anwaar Ulhaq
Formaat:	Artigo
Taal:	Inglês
Gepubliceerd in:	MDPI AG 2024-02-01
Reeks:	Remote Sensing
Onderwerpen:	segmentation convolutional neural network hyperspectral image (HSI) support vector machine (SVM) factor analysis (FA) multi-layer CNN
Online toegang:	https://www.mdpi.com/2072-4292/16/5/807
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