A Novel Approach of Hyperspectral Imaging Classification using Hybrid ConvNet

In recent years, remote sensing applications have been booming, and with this hyperspectral imaging (HSI) has been used in many real-life applications. However, the classification of HSI is a significant problem due to the complex features of the captured hyperspectral scene. Moreover, the HSI is often inherently nonlinear and has very high-dimensional data. Recent years have seen a rise in deep learning applications for addressing nonlinear problems. However, deep learning tends to overfit when sparse or less training data is available. In this paper, the proposed work focuses on addressing the trade-off problem between classification performance and less training samples for classifying hyperspectral image data in a single training process. Thus, the study presents a hybrid multilayer learning system based on the joint approach of 2D and 3D convolutional kernels. The main reason is to utilize the spectral-spatial and spatial correlations in the learning process to achieve improved generalization of features in the training process for better HSI classification. The study outcome exhibits higher precision, recall rate, and F1-score performance. The overall accuracy is 99.9%, with a better convergence rate. The results prove that the proposed model is effective for HSI classification even with fewer training data samples.