INFRARED SMALL TARGET DETECTION BASED ON SALIENCY AND PRINCIPLE COMPONENT ANALYSIS

Infrared small target detection is considered as a binary classification problem between target and background. According to the principle of point spread function (PSF), infrared small target training set was simulated. Principal component analysis (PCA) was used to extract the main characteristics of target sample. Thus, the principal component space of the target was established. Each test sample can be recognized as either target or background by its reconstruction error in the principle subspace. In order to improve the real-time performance, an infrared small target detection algorithm based on saliency and PCA was proposed. Salient regions probably containing targets were firstly detected by using spectral residual approach. Then target recognition was performed in the salient regions. Experimental results indicate that the proposed algorithm is fast and effective.