Dimensionality Reduction and Evaluation in Hyperspectral Images using LSTM Models

Development in the field of computer-aided learning and testing have stimulated the progress of novel and efficient knowledge-based expert systems, that have shown hopeful outcomes in a broad variety of practical applications. In particular, deep learning (DL) techniques have been extensively carried out to identify remote sensed data obtained by the instruments of Earth Observation. Hyperspectral imaging (HSI) is an evolving area in the study of remotely sensed data due to the huge volume of information found in these images, which enables better classification and processing of the Earth's surface by integrating ample of spatial and spectral features. This paper primarily deals with the Recurrent Neural Networks (RNNs), especially the Long Short Term Memory (LSTM) networks for HSI classification. Different variants of LSTM are analyzed and compared, along with other state-of-the-art models in terms of accuracy metrics using certain hyperspectral datasets. The paper concludes with the analysis of some emerging future research axes.