Quaternion based neural network for hyperspectral image classification

Shishir Paramathma Rao; Karen Panetta; Sos Agaian

doi:10.1117/12.2558808

26 May 2020 Quaternion based neural network for hyperspectral image classification

Shishir Paramathma Rao, Karen Panetta, Sos Agaian

Proceedings Volume 11399, Mobile Multimedia/Image Processing, Security, and Applications 2020; 113990S (2020) https://doi.org/10.1117/12.2558808
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Neural networks have emerged to be the most appropriate method for tackling the classification problem for hyperspectral images (HIS). Convolutional neural networks (CNNs), being the current state-of-art for various classification tasks, have some limitations in the context of HSI. These CNN models are very susceptible to overfitting because of 1) lack of availability of training samples, 2) large number of parameters to fine-tune. Furthermore, the learning rates used by CNN must be small to avoid vanishing gradients, and thus the gradient descent takes small steps to converge and slows down the model runtime. To overcome these drawbacks, a novel quaternion based hyperspectral image classification network (QHIC Net) is proposed in this paper. The QHIC Net can model both the local dependencies between the spectral channels of a single-pixel and the global structural relationship describing the edges or shapes formed by a group of pixels, making it suitable for HSI datasets that are small and diverse. Experimental results on three HSI datasets demonstrate that the Q-HIC Net performs on par with the traditional CNN based methods for HSI Classification with a far fewer number of parameters.

Conference Presentation

Citation Download Citation

Shishir Paramathma Rao, Karen Panetta, and Sos Agaian "Quaternion based neural network for hyperspectral image classification", Proc. SPIE 11399, Mobile Multimedia/Image Processing, Security, and Applications 2020, 113990S (26 May 2020); https://doi.org/10.1117/12.2558808

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