Hyperspectral Image Classification: Artifacts of Dimension Reduction on Hybrid CNN
This work addresses computational efficiency for hyperspectral image classification, but it is incremental as it builds on existing CNN methods.
The paper tackled the problem of high computational cost in hyperspectral image classification by proposing a lightweight hybrid CNN model (3D followed by 2D-CNN) with preprocessing, which outperformed state-of-the-art 2D/3D CNN models in generalization performance, statistical significance, and computational complexity on five benchmark datasets.
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.