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Title: IRSN: Involutional Residual Spectral Network for Hyperspectral Image Classification
Authors: Cihan, M.
Ceylan, M.
Keywords: Deep learning
hyperspectral image classification
involutional residual spectral network (IRSN)
remote sensing
Classification (of information)
Data mining
Deep learning
Hyperspectral imaging
Image classification
Neural networks
Principal component analysis
Remote sensing
Convolutional neural network
Deep learning
Features extraction
Hyperspectral image classification
Involutional residual spectral network
Principal-component analysis
Feature extraction
experimental study
extraction method
image classification
machine learning
remote sensing
spectral resolution
Publisher: Institute of Electrical and Electronics Engineers Inc.
Abstract: Convolutional methods are commonly used for hyperspectral imaging (HSI) classification. However, HSI datasets are large due to numerous narrow-band spectra, leading to high computational costs and optimization challenges in convolution-based deep learning models. To address this, we propose the involutional residual spectral network (IRSN), using involution kernels tailored to the data for meaningful feature extraction. IRSN achieves this with fewer parameters than convolutions. By leveraging involution layers based on spectral signatures, IRSN captures spectral-spatial information. Furthermore, residual blocks within the network facilitate information preservation and overcome gradient-related challenges. Experimental studies conducted using four publicly available datasets demonstrate that the proposed IRSN model outperforms certain state-of-the-art convolutional-based networks in terms of effectiveness and efficiency. © 2004-2012 IEEE.
ISSN: 1545-598X
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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