MFFCG – Multi feature fusion for hyperspectral image classification using graph attention network

dc.creatorAslam Bhatti, Uzair
dc.creatorHuang, Mengxing
dc.creatorNEIRA MOLINA, HAROLD ROBERTO
dc.creatorMarjan, Shah
dc.creatorBaryalai, Mehmood
dc.creatorTang, Hao
dc.creatorWu, Guilu
dc.creatorUllah Bazai, Sibghat
dc.date2023-07-27T14:15:18Z
dc.date2025-11-01
dc.date2023-07-27T14:15:18Z
dc.date2023-11-01
dc.date.accessioned2023-10-03T19:39:08Z
dc.date.available2023-10-03T19:39:08Z
dc.identifierUzair Aslam Bhatti, Mengxing Huang, Harold Neira-Molina, Shah Marjan, Mehmood Baryalai, Hao Tang, Guilu Wu, Sibghat Ullah Bazai, MFFCG – Multi feature fusion for hyperspectral image classification using graph attention network, Expert Systems with Applications, Volume 229, Part A, 2023, 120496, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2023.120496.
dc.identifier0957-4174
dc.identifierhttps://hdl.handle.net/11323/10346
dc.identifier10.1016/j.eswa.2023.120496
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9171052
dc.descriptionClassification methods that are based on hyperspectral images (HSIs) are playing an increasingly significant role in the processes of target detection, environmental management, and mineral mapping as a result of the fast development of hyperspectral remote sensing technology. Improving classification performance is still a significant problem, however, as a result of the high dimensionality and redundancy of hyperspectral image sets (HSIs), as well as the presence of class imbalance in hyperspectral datasets. In the past few years, convolutional neural networks (CNNs) and graph convolutional networks (GCNs) have achieved good results in HSI classification, but CNNs struggle to achieve good accuracy in low samples, while GCNs have a huge computational cost. To resolve these issues, this paper proposes a Multi-Feature Fusion of 3D-CNN and Graph Attention Network MFFCG. The algorithm consists of two elements: the 3D-CNN, which produces good classification for 3D HSI cube data, and GAT-based encoder and decoder modules that help in improving the classification accuracy of the 3D-CNN. Finally, the multiple features are merged with the help of two neural network models. We further develop two optimized GAT models named GAT1 and GAT2, which are used with different layers of 3D-CNN. Algorithms after merging with 3D-CNN are named MFFCG-1 and MFFCG-2, which produce better classification results then other developed methods. Experiments on three public HSI datasets show that the proposed methods perform better than other state-of-the-art methods using the limited training samples and in low classification time.
dc.format76 páginas
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherElsevier Ltd.
dc.publisherUnited Kingdom
dc.relationExpert Systems with Applications
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dc.relation229
dc.rights© 2023 Elsevier Ltd. All rights reserved.
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.sourcehttps://www.sciencedirect.com/science/article/abs/pii/S0957417423009983
dc.subjectFeature fusion
dc.subject3D-CNN
dc.subjectGraph Attention Network
dc.subjectHSI
dc.titleMFFCG – Multi feature fusion for hyperspectral image classification using graph attention network
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_2df8fbb1
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/draft
dc.typehttp://purl.org/coar/version/c_b1a7d7d4d402bcce


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