  Hyperspectral Image (HSI) classification using Convolutional Neural Networks
(CNN) is widely found in the current literature. Approaches vary from using
SVMs to 2D CNNs, 3D CNNs, 3D-2D CNNs. Besides 3D-2D CNNs and FuSENet, the other
approaches do not consider both the spectral and spatial features together for
HSI classification task, thereby resulting in poor performances. 3D CNNs are
computationally heavy and are not widely used, while 2D CNNs do not consider
multi-resolution processing of images, and only limits itself to the spatial
features. Even though 3D-2D CNNs try to model the spectral and spatial features
their performance seems limited when applied over multiple dataset. In this
article, we propose SpectralNET, a wavelet CNN, which is a variation of 2D CNN
for multi-resolution HSI classification. A wavelet CNN uses layers of wavelet
transform to bring out spectral features. Computing a wavelet transform is
lighter than computing 3D CNN. The spectral features extracted are then
connected to the 2D CNN which bring out the spatial features, thereby creating
a spatial-spectral feature vector for classification. Overall a better model is
achieved that can classify multi-resolution HSI data with high accuracy.
Experiments performed with SpectralNET on benchmark dataset, i.e. Indian Pines,
University of Pavia, and Salinas Scenes confirm the superiority of proposed
SpectralNET with respect to the state-of-the-art methods. The code is publicly
available in https://github.com/tanmay-ty/SpectralNET.
