  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.
