Hyperspectral Image Classification Using Deep Learning Models: A Review
Deepak Kumar, Dharmender Kumar
Abstract
Abstract Hyperspectral image (HSI) classification is one of the important topic in the field of remote sensing. In general, HSI has to deal with complex characteristics and nonlinearity among the hyperspectral data which makes the classification task very challenging for traditional machine learning (ML) models. Recently, deep learning (DL) models have been very widely used in the classification of HSIs because of their capability to deal with complexity and nonlinearity in data. The utilization of deep learning models has been very successful and demonstrated good performance in the classification of HSIs. This paper presents a comprehensive review of deep learning models utilized in HSI classification literature and a comparison of various deep learning strategies for this topic. Precisely, the authors have categorized the literature review based upon the utilization of five most popular deep learning models and summarized their main methodologies used in feature extraction. This work may provide useful guidelines for the future research work in this area.