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A multi-component based volumetric directional pattern for texture feature extraction from hyperspectral imagery
Author(s): Paheding Sidike; Vasit Sagan; Vijayan Asari; Maitiniyazi Maimaitijiang
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Paper Abstract

Texture information has shown a significant contribution to pattern recognition in hyperspectral image (HSI) analysis. In this paper, a multi-component based the volumetric directional pattern (MC-VDP) is proposed for HSI classification. The original VDP operator extracts a three-dimensional texture feature from three consecutive bands by applying eight directional Kirsch filters to the raw intensity values. However, the local sign and local magnitude components, that are generated by a local difference sign-magnitude transform, are not incorporated before Kirsch masking. In this work, we first compute the local sign and local magnitude components followed by VDP operator and then combine them with the original VDP feature to form MC-VDP. By analyzing the local sign and local magnitude components, two volumetric texture features are obtained, namely VDP-Sign (VDP-S) and VDP-Magnitude (VDP-M). Thus MC-VDP operator is constituted of VDP-S, VDP-M, and the original VDP features. In details, VDP-S and VDP-M preserve additional discriminant information to describe the volumetric local structures in HSI, and they can be readily fused since their scheme are constructed in the same fashion. From experimental results, it is observed that a fusion of VDP-S, VDP-M, and the original VDP coded maps provides more discriminant information and thus better classification accuracy compared to the other popular spatial feature extraction methods.

Paper Details

Date Published: 30 April 2018
PDF: 9 pages
Proc. SPIE 10649, Pattern Recognition and Tracking XXIX, 1064910 (30 April 2018); doi: 10.1117/12.2309317
Show Author Affiliations
Paheding Sidike, Saint Louis Univ. (United States)
Vasit Sagan, Saint Louis Univ. (United States)
Vijayan Asari, Univ. of Dayton (United States)
Maitiniyazi Maimaitijiang, Saint Louis Univ. (United States)

Published in SPIE Proceedings Vol. 10649:
Pattern Recognition and Tracking XXIX
Mohammad S. Alam, Editor(s)

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