Small Infrared Target Detection Based on Kernel Principal Component Analysis

Small infrared target is very difficult to detect due to its own characteristics and complex background. In this paper, we present a small target detection method based on kernel principal component analysis (KPCA). First of all, small target samples are generated by using Gaussian intensity functions. Then a linear PCA is performed in feature space after the small target samples are mapped to a high-dimensional feature space via a nonlinear kernel function, and then the target-enhanced image is obtained by computing the distances between the projection vectors of the training samples and the projection vectors of the each block of the detecting images. Finally, the small infrared target is detected by segmenting the target-enhanced image adaptively. We choose some representative infrared images to evaluate the proposed method, and the experiment results show that the algorithm can detect the small infrared targets effectively.