Scale-space theory-based multi-scale features for aircraft classification using HRRP

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.