The Classification of Hyperspectral Images: A Double-Branch Multi-Scale Residual Network

Abstract

With the continuous advancement of deep learning technology, researchers have made further progress in the hyperspectral image (HSI) classification domain. We propose a double-branch multi-scale residual network (DBMSRN) framework for HSI classification to improve classification accuracy and reduce the number of required training samples. The DBMSRN consists of two branches designed to extract spectral and spatial features from the HSI. Thus, to obtain more comprehensive feature information, we extracted additional local and global features at different scales by expanding the network width. Moreover, we also increased the network depth to capture deeper feature information. Based on this concept, we devise spectral multi-scale residuals and spatial multi-scale residuals within a double-branch architecture. Additionally, skip connections are employed to augment the context information of the network. We demonstrate that the proposed framework effectively enhances classification accuracy in scenarios with limited training samples through experimental analysis. The proposed framework achieves an overall accuracy of 98.67%, 98.09%, and 96.76% on the Pavia University (PU), Kennedy Space Center (KSC), and Indian Pines (IP) datasets, respectively, surpassing the classification accuracy of existing advanced frameworks under identical conditions.

A Hybrid-Scale Feature Enhancement Network for Hyperspectral Image Classification

“…A hyperspectral image (HSI) possesses plentiful spectral and spatial information, which has been widely adopted in extensive application areas, such as precision agriculture [3], environmental monitoring [4], mineral exploration [5], and urban planning [6]. HSI classification has become a research hotspot in pattern recognition and image processing, which is devoted to assigning a unique category label to each spatial pixel [7][8][9][10]. However, HSI classification is still a challenging issue, i.e., especially spatial variability and the curse of dimensionality, thereby increasing the difficulty of classification.…”