Multi-Scale Hybrid Spectral Network for Feature Learning and Hyperspectral Image Classification

Abstract

Hyperspectral image (HSI) classification is an important concern in remote sensing, but it is complex since few numbers of labelled training samples and the high-dimensional space with many spectral bands. Hence, it is essential to develop a more efficient neural network architecture to improve performance in the HSI classification task. Deep learning models are contemporary techniques for pixel-based hyperspectral image (HSI) classification. Deep feature extraction from both spatial and spectral channels has led to high classification accuracy. Meanwhile, the effectiveness of these spatial-spectral methods relies on the spatial dimension of every patch, and there is no feasible method to determine the best spatial dimension to take into consideration. It makes better sense to retrieve spatial properties through examination at different neighborhood scales in spatial dimensions. In this context, this paper presents a multi-scale hybrid spectral convolutional neural network (MS-HybSN) model that uses three distinct multi-scale spectral-spatial patches to pull out properties in spectral and spatial domains. The presented deep learning framework uses three patches of different sizes in spatial dimension to find these possible features. The process of Hybrid convolution operation (3D-2D) is done on each selected patch and is repeated throughout the image. To assess the effectiveness of the presented model, three benchmark datasets that are openly accessible (Pavia University, Indian Pines, and Salinas) and new Indian datasets (Ahmedabad-1 and Ahmedabad-2) are being used in experimental studies. Empirically, it has been demonstrated that the presented model succeeds over the remaining state-of-the-art approaches in terms of classification performance.