Multi-head attention with CNN and wavelet for classification of hyperspectral image

Hyperspectral Image (HSI) is characterized by large number of bands with a high spectral resolution where continuous spectrum is measured for each pixel. This high volume therefore leads to challenges in processing the dataset. Objective of Dimensionality Reduction (DR) algorithms is to identify and eliminate statistical redundancies of hyperspectral data while keeping as much spectral information as possible. Combining spectral and spatial information offers a more comprehensive classification approach. Convolutional neural network (CNN) has the potential to extract complex spatial and spectral features embedded in Hyperspectral data. Wavelet transform belongs to the family of multi-scale transformation where the input signal is analyzed at different levels of granularity. Attention mechanism is a method in neural networks to guide the algorithm to focus on the important information in the data. In this paper, we use Multi-head Transformer-based Attention (Vaswani et al. in Attention is all you Need, 2017) technique for Channel attention which captures the long-range spectral dependencies. The experimental results show that the proposed algorithm MT-CW Band Selection-based multi-head transformer for dimensionality reduction and Wavelet CNN-based algorithm for feature extraction yields impressive results in terms of information conservation and class separability.