Balkan Journal of Electrical and Computer Engineering 2147-284X 2147-284X MUSA YILMAZ 10.17694/bajece.1039029 Artificial Intelligence Yapay Zeka Hybrid 3D Convolution and 2D Depthwise Separable Convolution Neural Network for Hyperspectral Image Classification https://orcid.org/0000-0002-1257-8518 Fırat Hüseyin Dicle Üniversitesi https://orcid.org/0000-0003-4585-4168 Asker Mehmet Emin DICLE UNIVERSITY https://orcid.org/0000-0003-2271-7865 Hanbay Davut İNÖNÜ ÜNİVERSİTESİ 01 30 2022 10 1 35 46 12 21 2021 01 21 2022 Copyright © 2013, Balkan Journal of Electrical and Computer Engineering 2013 Balkan Journal of Electrical and Computer Engineering

Convolutional neural networks (CNNs) are one of the popular deep learning methods used to solve the hyperspectral image classification (HSIC) problem. CNN has a strong feature learning ability that can ensure more distinctive features for higher quality HSIC. The traditional CNN-based methods mainly use the 2D CNN for HSIC. However, with 2D CNN, only spatial features are extracted in HSI. Good feature maps cannot be extracted from spectral dimensions with the use of 2D CNN alone. By using 3D CNN, spatial-spectral features are extracted simultaneously. However, 3D CNN is computationally complex. In this study, a hybrid CNN method, which is a combination of 3D CNN and 2D CNN, is improved to solve the two problems described above. Using hybrid CNN decreases the complexity of the method compared to using only 3D CNN and can perform well against a limited number of training samples. On the other hand, in Hybrid CNN, depthwise separable convolution (DSC) is used, which decreases computational cost, prevents overfitting and enables more spatial feature extraction. By adding DSC to the developed hybrid CNN, a hybrid depthwise separable convolutional neural network is obtained. Extensive applications on frequently used HSI benchmark datasets show that the classification performance of the proposed network is better than compared methods.

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