Abstract:
The use of Hyperspectral Image(HSI) has become prevalent in many sectors due to its ability to identify detailed spectral
information (i.e., relationships between the collected spectral data and the object in the HSI data) that cannot be obtained through
ordinary imaging. Traditional RGB image classification approaches are insufficient for hyperspectral image classification(HSIC)
because they struggle to capture the subtle spectral information that exists within hyperspectral data. In the past few years, the Deep
Learning(DL) based model has become a very powerful and efficient non-linear feature extractor for a wide range of computer
vision tasks. Furthermore, DL-based models are exempt from manual feature extraction. The use of this stimulus prompted the
researchers to use a DL-based model for the classification of Hyperspectral Images, which yielded impressive results. This motivation
inspired the researchers to develop a DL-based model for the classification of hyperspectral images, which performed well. Deeper
networks might encounter vanishing gradient problems, making optimization more difficult. To address this issue, regularisation
and architectural improvements are being implemented. One of the key issues is that the DL-based HSIC model requires a large
number of training samples for training, which is an important concern with hyperspectral data due to the scarcity of public
HSI datasets. This article provides an overview of deep learning for hyperspectral image classification and assesses the most recent
methods. Among all studied methods SpectralNET offers significantly better performance, due to the utilization of wavelet transformation.
