Dimensionality Reduction and Classification in Hyperspectral Images Using Deep Learning

Abstract

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 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. Nevertheless, because of the high-dimensional data and restricted training samples available, HSI presents some crucial challenges for classification of supervised methods. In particular, it addresses the problems of spectral and spatial resolution, volume of data, and model conversion from multimedia images to hyperspectral data. Various deep learning-based architectures are currently being established to solve these limitations, showing significant results in the analysis of hyperspectral data. In this paper, we deal primarily with the hyperspectral datasets, the dimensionality curse problem, and methods for classifying those datasets using some deep neural networks (DNN), especially convolutional neural networks (CNN). We provide a comparative analysis of various dimensionality reduction (DR) and classification techniques used for finding accuracies based on the datasets used. We also explore certain hyperspectral imaging applications along with some of the research axes.