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.
Paheding Sidike, Vasit Sagan, Vijayan Asari, and Maitiniyazi Maimaitijiang, "A multi-component based volumetric directional pattern for texture feature extraction from hyperspectral imagery ," Proc. SPIE 10649, Pattern Recognition and Tracking XXIX, 1064910 (Presented at SPIE Defense + Security: April 19, 2018; Published: 30 April 2018); https://doi.org/10.1117/12.2309317.
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