Multiview Feature-Based Sea Surface Small Target Detection in Short Observation Time

In this letter, a small target detection method in sea clutter background is proposed based on multiview polarization features and localized one-class support vector machine (LOCSVM). In the proposed method, three views of features that represent the divergence between the small target and the clutter are extracted: 1) the study of the target scattering characteristics shows that the dominant scattering components of the small target cell (TC) are sphere scattering and diplane scattering, while the scattering mechanism of the clutter cell is varied and affected by the sea conditions. Therefore, the relative powers of the sphere and diplane scattering components are extracted based on the polarimetric scattering matrix; 2) Because the target is a definite shaped object, its cell does not exist in the fractal characteristics, while the clutter cell is on the contrary. Therefore, the average Tsallis entropy (TE) is extracted from full-polarization channels to reflect the fractal characteristics of the cell; and 3) As the generalization of the Doppler entropy, because the frequency spectrum of the TC is more congregate, the TE can reflect the Doppler characteristics. Then, in the detection stage, a novel one-class classifier, referred to as LOCSVM, is designed as the detector. LOCSVM is a combination of
$k$
-means clustering and OCSVM and can enhance the detection performance of OCSVM. The experimental results on the IPIX data show the effectiveness of the proposed method, especially in the case of short observation time.