Open AccessScale‐space theory‐based multi‐scale features for aircraft classification using HRRP
Author(s) -
Liu Jia,
Fang Ning,
Jun Xie Yong,
Fa Wang Bao
Publication year - 2016
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2015.3583
Subject(s) - scale (ratio) , range (aeronautics) , divergence (linguistics) , pattern recognition (psychology) , scale space , artificial intelligence , radar , computer science , feature extraction , feature (linguistics) , feature vector , feature selection , automatic target recognition , support vector machine , data mining , mathematics , synthetic aperture radar , engineering , geography , cartography , aerospace engineering , image processing , image (mathematics) , telecommunications , linguistics , philosophy
High‐resolution range profile is the significant characteristic of radar targets in automatic target recognition. Traditional feature extractions of range profiles in target classification are constrained to the original scale. This Letter proposes a multi‐scale target classification method based on the scale‐space theory. Target range profile feature is extended from single scale to multiple scales. The minimum Kullback–Leibler mean divergence (MKMD) algorithm is developed to achieve the automatic optimal scale factor selection. Classification evaluations on aircraft models using support vector machine and 3‐nearest neighbour classifiers demonstrate that the application of scale‐space theory in multi‐scale feature extraction could effectively enhance the classification performance. The feasibility of the proposed MKMD algorithm is also validated by an enumeration method.