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The high dimensionality of hyperspectral remote sensing images (HRSI) affects the classification performance. Therefore, most HRSI classification methods use dimension reduction methods as a solution for high dimensionality. It is aimed to extract useful features with dimension reduction methods. At the end of this process, the data dimension is reduced and the transaction cost is decreased. In this study, LDA, PCA, IPCA, ICA, SPCA, RPCA and SVD dimension reduction methods were applied as a preprocessing step to improve HRSI classification performance. Since HRSI is volumetric data and has a spectral dimension, 2D CNN cannot extract good distinguishing features from spectral dimensions. Because 2D CNN only considers spatial information. With 3D CNN, spectral-spatial features are extracted simultaneously. However, 3D CNN increases the computational cost. Therefore, in this study, Hybrid 3D/2D CNN method is used together with dimension reduction methods. Hybrid CNN method consists of a combination of 3D CNN, 2D CNN and depthwise separable convolution. While 3D CNN extracts common spectral-spatial features, more spatial features are learned with 2D CNN used after 3D CNN. With depthwise separable convolution, it reduces the number of parameters and overfitting is prevented. The applications performed on the frequently used HRSI benchmark datasets show that the classification performance of the proposed method is better than the compared methods. In addition, Indian pines, HyRANK-Loukia, Botswana and Pavia of University datasets are used to examine the effect of dimension reduction methods used together with the hybrid 3D/2D CNN method on classification performance. As a result of the applications, the best classification accuracies were obtained in PCA, LDA and IPCA with Indian pines, PCA with Pavia of university, PCA and IPCA with Salinas, PCA, RPCA and LDA dimension reduction methods with HyRANK-Loukia. © 2022 Elsevier B.V. |
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