Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/5017
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Cihan, M. | - |
dc.contributor.author | Ceylan, M. | - |
dc.date.accessioned | 2024-01-23T09:29:42Z | - |
dc.date.available | 2024-01-23T09:29:42Z | - |
dc.date.issued | 2024 | - |
dc.identifier.issn | 1545-598X | - |
dc.identifier.uri | https://doi.org/10.1109/LGRS.2023.3341497 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/5017 | - |
dc.description.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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.relation.ispartof | IEEE Geoscience and Remote Sensing Letters | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Deep learning | en_US |
dc.subject | hyperspectral image classification | en_US |
dc.subject | involution | en_US |
dc.subject | involutional residual spectral network (IRSN) | en_US |
dc.subject | remote sensing | en_US |
dc.subject | Classification (of information) | en_US |
dc.subject | Convolution | en_US |
dc.subject | Data mining | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Extraction | en_US |
dc.subject | Hyperspectral imaging | en_US |
dc.subject | Image classification | en_US |
dc.subject | Neural networks | en_US |
dc.subject | Principal component analysis | en_US |
dc.subject | Remote sensing | en_US |
dc.subject | Convolutional neural network | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Features extraction | en_US |
dc.subject | Hyperspectral image classification | en_US |
dc.subject | Involution | en_US |
dc.subject | Involutional residual spectral network | en_US |
dc.subject | Kernel | en_US |
dc.subject | Principal-component analysis | en_US |
dc.subject | Remote-sensing | en_US |
dc.subject | Feature extraction | en_US |
dc.subject | experimental study | en_US |
dc.subject | extraction method | en_US |
dc.subject | image classification | en_US |
dc.subject | layer | en_US |
dc.subject | machine learning | en_US |
dc.subject | remote sensing | en_US |
dc.subject | spectral resolution | en_US |
dc.title | IRSN: Involutional Residual Spectral Network for Hyperspectral Image Classification | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1109/LGRS.2023.3341497 | - |
dc.identifier.scopus | 2-s2.0-85180339845 | en_US |
dc.department | KTÜN | en_US |
dc.identifier.volume | 21 | en_US |
dc.identifier.startpage | 1 | en_US |
dc.identifier.endpage | 5 | en_US |
dc.identifier.wos | WOS:001129709800003 | en_US |
dc.institutionauthor | … | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 57226111647 | - |
dc.authorscopusid | 56276648900 | - |
item.fulltext | No Fulltext | - |
item.openairetype | Article | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | none | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections |
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