Spatial-spectral classification of hyperspectral remote sensing images using 3D CNN based LeNet-5 architecture

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dc.authoridHanbay, Davut/0000-0003-2271-7865
dc.authoridFIRAT, Huseyin/0000-0002-1257-8518
dc.authoridBAYINDIR, Mehmet ilyas/0000-0003-1999-014X
dc.authorwosidHanbay, Davut/AAG-8511-2019
dc.authorwosidFIRAT, Huseyin/ABB-7417-2021
dc.authorwosidBAYINDIR, Mehmet ilyas/V-6198-2018
dc.contributor.authorFirat, Hueseyin
dc.contributor.authorAsker, Mehmet Emin
dc.contributor.authorBayindir, Mehmet Ilyas
dc.contributor.authorHanbay, Davut
dc.date.accessioned2024-08-04T20:53:12Z
dc.date.available2024-08-04T20:53:12Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractHyperspectral remote sensing image (HRSI) analysis are commonly used in a wide variety of remote sensing applications such as land cover analysis, military surveillance, object detection and precision agriculture. Deep learning is seen as an effective method to improve HRSI classification. In particular, convolutional neural net-works (CNNs) are increasingly used in this field. The high dimensionality of the HRSIs increases the computa-tional complexity. Thus, most of studies apply dimension reduction as preprocessing. Another problem in HRSI classification is that spatial-spectral features must be considered in order to obtain accurate results. Because, HRSI classification results are highly dependent on spatiospectral information. The aim of this paper is to build a 3D CNN-based LeNet-5 method for HRSI classification. Principal component analysis (PCA) is used as the pre-processing step for optimum spectral band extraction. 3D CNN is used to simultaneously extract spatial -spectral features in HRSIs. LeNet-5 architecture has a simple and straightforward architecture. At the same time, the number of trainable parameters is very low. With the use of the LeNet-5 architecture, the number of trainable parameters of the proposed method is considerably reduced. This is one of the most important features that distinguish the proposed method from other deep learning methods. The proposed method is tested with Indian pines, Pavia University and Salinas datasets. As a result of experimental studies, 100% overall accuracy result is obtained in all datasets. The proposed 3DLeNet method is compared against various state-of-the-art CNN based methods. From the experimental results, it is seen that our 3DLeNet method performs more accurate result. It has also been found that the proposed 3DLeNet method shows a satisfactory result with less computational complexity.en_US
dc.identifier.doi10.1016/j.infrared.2022.104470
dc.identifier.issn1350-4495
dc.identifier.issn1879-0275
dc.identifier.scopus2-s2.0-85143052149en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1016/j.infrared.2022.104470
dc.identifier.urihttps://hdl.handle.net/11616/101027
dc.identifier.volume127en_US
dc.identifier.wosWOS:000900800300006en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofInfrared Physics & Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectRemote Sensingen_US
dc.subjectHyperspectral image classificationen_US
dc.subjectLeNet-5 architectureen_US
dc.subject3D convolutional neural networken_US
dc.subjectPrincipal component analysisen_US
dc.titleSpatial-spectral classification of hyperspectral remote sensing images using 3D CNN based LeNet-5 architectureen_US
dc.typeArticleen_US

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