Hyperspectral Image Classification: Artifacts of Dimension Reduction on Hybrid CNN

TL;DR

This paper introduces a lightweight hybrid CNN model for hyperspectral image classification that balances spectral-spatial feature extraction with reduced computational cost, outperforming existing models on multiple datasets.

Contribution

A novel hybrid CNN architecture combining 3D and 2D CNNs with preprocessing to improve efficiency and accuracy in hyperspectral image classification.

Findings

Outperforms state-of-the-art 2D/3D CNNs in accuracy and efficiency

Reduces computational complexity significantly

Achieves better generalization across multiple datasets

Abstract

Convolutional Neural Networks (CNN) has been extensively studied for Hyperspectral Image Classification (HSIC) more specifically, 2D and 3D CNN models have proved highly efficient in exploiting the spatial and spectral information of Hyperspectral Images. However, 2D CNN only considers the spatial information and ignores the spectral information whereas 3D CNN jointly exploits spatial-spectral information at a high computational cost. Therefore, this work proposed a lightweight CNN (3D followed by 2D-CNN) model which significantly reduces the computational cost by distributing spatial-spectral feature extraction across a lighter model alongside a preprocessing that has been carried out to improve the classification results. Five benchmark Hyperspectral datasets (i.e., SalinasA, Salinas, Indian Pines, Pavia University, Pavia Center, and Botswana) are used for experimental evaluation. The experimental results show that the proposed pipeline outperformed in terms of generalization performance, statistical significance, and computational complexity, as compared to the state-of-the-art 2D/3D CNN models except commonly used computationally expensive design choices.