A comprehensive systematic review of deep learning methods for hyperspectral images classification

The remarkable growth of deep learning (DL) algorithms in hyperspectral images (HSIs) in recent years has garnered a lot of research space. This study examines and analyses over 250 newly proposed breakthroughs, the majority from 2019 onwards, that focussed on DL methods for HSI classification. Initially, a meta-analysis was performed to analyse the state-of-the-art literature, followed by an in-depth investigation of the performance of DL models on benchmark hyperspectral datasets. It provides an adequate grasp of how DL models are employed with the suggested improvisations and evaluates the performance of these state-of-the-art models. It investigates the accuracy of benchmark hyperspectral datasets by applying the newly proposed DL models. This study discusses the challenges that researchers have faced and the solutions that have been proposed. In particular, the scarcity of non-traditional hyperspectral data sets with labels impedes the performance of novel DL algorithms. As a result, this article clarifies the supervised, semi-supervised, and unsupervised training modes employed by the models and analyses the performance of the DL models while utilizing them.