Dimensionality reduction strategies for land use land cover classification based on airborne hyperspectral imagery: a survey

Hyperspectral image (HSI) contains hundreds of adjacent spectral bands, which can effectively differentiate the region of interest. Nevertheless, many irrelevant and highly correlated spectral bands lead to the Hughes phenomenon. Consequently, hyperspectral image dimensionality reduction is necessary to select the most informative and significant spectral band and eliminate the redundant spectral band. To this end, this paper represents an extensive and systematic survey of hyperspectral dimensionality reduction approaches for land use land cover (LULC) classification. Moreover, this paper reviewed the following important points: (1) hyperspectral imaging data acquisition methods, (2) the difference between hyperspectral and multispectral images, (3) hyperspectral image dimensionality reduction based on machine learning (ML) and deep learning (DL) techniques, (4) the popular benchmark hyperspectral datasets with the performance metrics for LULC classification, and (5) the significant challenges with the future trends for hyperspectral dimensionality reduction.

Automated summary

This paper provides an extensive and systematic survey of hyperspectral dimensionality reduction approaches for land use land cover (LULC) classification. It covers methods of acquiring hyperspectral imaging data, the difference between hyperspectral and multispectral images, dimensionality reduction based on machine learning and deep learning techniques, popular benchmark datasets for LULC classification, and significant challenges and future trends.