Special section guest editorial : Advances in deep learning for hyperspectral image analysis and classification

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Zareappor, Masoumeh and Ren, Jinchang and Zhou, Huiyu and Yang, Wankou (2019) Special section guest editorial : Advances in deep learning for hyperspectral image analysis and classification. Journal of Applied Remote Sensing, 13 (2). 022001. ISSN 1931-3195 (https://doi.org/10.1117/1.JRS.13.022001)

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Abstract

Remote sensing is a classical area of research that has been involved in many crucial applications, including urban development agriculture, scene interpretation, defense, weather, and other non-Earth observations. In the last decade, the analysis of hyperspectral images (HSIs) acquired by remote sensors has gained substantial attention and is increasingly becoming an active research discipline. However, there are some main challenges in hyperspectral data classification, such as ultra-high dimensionality of data, a limited number of labeled instances, and large spatial variability of spectral signature. These challenges degrade the ability to differentiate the pairwise distance between points and make it difficult to discriminate the most relevant features, causing the classification performance to give wrong or inaccurate results. Therefore, in processing hyperspectral images, the classification approaches have been proposed jointly by dimensionality reduction. Several feature extraction based HSI have been developed to solve the classification problem in hyperspectral images. These methods aim to reduce the dimensionality of the data while preserving the discriminative information of both spectral and spatial features.

ORCID iDs

Zareappor, Masoumeh, Ren, Jinchang ORCID logoORCID: https://orcid.org/0000-0001-6116-3194, Zhou, Huiyu and Yang, Wankou;
CORE (COnnecting REpositories)