Comparison of Small-Sized Deep Neural Network Models for Hyperspectral Image Classification

Ekrem Tarık Karan*, Zümray Dokur, Tamer Ölmez

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Many different methods related to deep learning are used in a broad scope in areas such as identification, clustering, and classification of 1D, 2D, and 3D data. In contrast to traditional images, deep learning studies are also performed on hyperspectral images, which are structurally different by the hundreds of bands it contains. Although hyperspectral images are data in 3D format, they can be analyzed separately in 1D, 2D and 3D formats, which is advantageous for deep learning research. Although the presence of so many different types of information provides diversity for studies, on the other hand, it significantly affects the values and working times of the results. In the scope of this study, the classification of hyperspectral images using convolutional neural networks, a subtitle of deep learning, was carried out. The classification results obtained by convolutional neural networks with different characteristics and changing parameters were examined. In this way, the effect of other methods on the classification results was also discussed. Within the scope of the whole study, all methods were tested on Indian Pines, Salinas and Pavia University datasets, frequently preferred in the literature. The results obtained with the methods we used were compared with the results of other studies in the literature. It has been confirmed that the network models with the 3D convolution layer mentioned in the literature and our studies have better performance results. Furthermore, although our proposed models contain fewer parameters, unlike the models in the literature, they almost approach or exceed the other models’ results.

Original languageEnglish
Title of host publicationComputational Intelligence, Data Analytics and Applications - Selected papers from the International Conference on Computing, Intelligence and Data Analytics ICCIDA
EditorsFausto Pedro García Márquez, Akhtar Jamil, Süleyman Eken, Alaa Ali Hameed
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)9783031270987
Publication statusPublished - 2023
EventInternational Conference on Computing, Intelligence and Data Analytics, ICCIDA 2022 - Kocaeli, Turkey
Duration: 16 Sept 202217 Sept 2022

Publication series

NameLecture Notes in Networks and Systems
Volume643 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389


ConferenceInternational Conference on Computing, Intelligence and Data Analytics, ICCIDA 2022

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.


The environment in which deep learning applications are run seriously affects the results of the study. For this reason, we would like to thank the National HighPerformance Computing Application and Research Center (UHeM) at Istanbul Technical University for providing its quality hardware.

FundersFunder number
National HighPerformance Computing Application and Research Center
Ulusal Yüksek Başarımlı Hesaplama Merkezi, Istanbul Teknik Üniversitesi
Istanbul Teknik Üniversitesi


    • Convolutional neural networks
    • Hyperspectral image classification
    • Incremental principal component analysis
    • Principal component analysis


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