3D Octave and 2D Vanilla Mixed Convolutional Neural Network for Hyperspectral Image Classification with Limited Samples

• Published in: Remote sensing (Basel, Switzerland) Vol. 13; no. 21; p. 4407
• Main Authors: Feng, Yuchao, Zheng, Jianwei, Qin, Mengjie, Bai, Cong, Zhang, Jinglin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2021
• Subjects: 3D octave and 2D vanilla mixed convolutions; 3D-2D mixed convolutional neural network (MCNN); Artificial neural networks; Classification; Computer applications; Computing costs; Deep learning; Discriminant analysis; Feature extraction; Feature maps; Homology; homology shifting; hyperspectral image (HSI) classification; Hyperspectral imaging; Image classification; Neural networks; principal component analysis (PCA)
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13214407

Owing to the outstanding feature extraction capability, convolutional neural networks (CNNs) have been widely applied in hyperspectral image (HSI) classification problems and have achieved an impressive performance. However, it is well known that 2D convolution suffers from the absent consideration of spectral information, while 3D convolution requires a huge amount of computational cost. In addition, the cost of labeling and the limitation of computing resources make it urgent to improve the generalization performance of the model with scarcely labeled samples. To relieve these issues, we design an end-to-end 3D octave and 2D vanilla mixed CNN, namely Oct-MCNN-HS, based on the typical 3D-2D mixed CNN (MCNN). It is worth mentioning that two feature fusion operations are deliberately constructed to climb the top of the discriminative features and practical performance. That is, 2D vanilla convolution merges the feature maps generated by 3D octave convolutions along the channel direction, and homology shifting aggregates the information of the pixels locating at the same spatial position. Extensive experiments are conducted on four publicly available HSI datasets to evaluate the effectiveness and robustness of our model, and the results verify the superiority of Oct-MCNN-HS both in efficacy and efficiency.