Optimal Spatial-Spectral Input For Real-Time Hyperspectral Image Classification

In this paper, we propose a novel approach for real-time hyperspectral image classification using optimal spatial-spectral input. The optimal input consisted of the main pixel with two of its spatial neighbors. We developed a lightweight deep neural network model consisting of only 5705 parameters and trained it with the well-known Pavia University dataset. Our model achieved a high overall accuracy of 96%. Furthermore, in terms of latency, our approach maximized the preprocessing speed compared to other spatial-spectral methods. The attained fast speed in preprocessing and inference is applicable for Real-time application.