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

Masoumeh Zareappor, Jinchang Ren, Huiyu Zhou, Wankou Yang

Research output: Contribution to journalEditorial

1 Citation (Scopus)
18 Downloads (Pure)


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.

Original languageEnglish
Article number022001
Number of pages2
JournalJournal of Applied Remote Sensing
Issue number2
Publication statusPublished - 7 Feb 2019


  • remote sensing
  • hyperspectral image classification
  • hyperspectral image analysis


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