Hyperspectral Image Classification using the MRELBP Texture Descriptor

This paper presents an extension of the Local Binary Patterns feature descriptors to hyperspectral image classification. Our approach uses a Principal Component Analysis technique to extract the most representative bands and for each pixel, it concatenates the histograms obtained for each selected b...

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Bibliographic Details
Main Authors: Barburiceanu, Stefania, Terebes, Romulus, Meza, Serban
Format: Conference Proceeding
Language:English
Subjects:
feature extraction
hyperspectral image classification
Local Binary Patterns
Online Access:Request full text
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Summary:This paper presents an extension of the Local Binary Patterns feature descriptors to hyperspectral image classification. Our approach uses a Principal Component Analysis technique to extract the most representative bands and for each pixel, it concatenates the histograms obtained for each selected band. The histograms are built from features which are discriminative and invariant to different transformations in the input. The proposed method achieves promising results for hyperspectral image classification. We tested our technique on four publicly available hyperspectral image databases of Earth observation images and for all of them the proposed method improved the classification accuracy when compared to the classical Local Binary Pattern approach.
ISSN:2575-5145
DOI:10.1109/EHB47216.2019.8969874