Small-floating Target Detection in Sea Clutter via Visual Feature Classifying in the Time-Doppler Spectra
This addresses a domain-specific challenge for radar systems in maritime surveillance, with incremental improvements over prior approaches.
The paper tackles the problem of detecting small-floating objects in sea clutter for surface radar by observing that target backscatters disrupt motion continuity in time-Doppler spectra images, and it uses local binary pattern features with an unsupervised SVM to achieve favorable detection rates compared to existing methods on real-life IPIX radar data.
It is challenging to detect small-floating object in the sea clutter for a surface radar. In this paper, we have observed that the backscatters from the target brake the continuity of the underlying motion of the sea surface in the time-Doppler spectra (TDS) images. Following this visual clue, we exploit the local binary pattern (LBP) to measure the variations of texture in the TDS images. It is shown that the radar returns containing target and those only having clutter are separable in the feature space of LBP. An unsupervised one-class support vector machine (SVM) is then utilized to detect the deviation of the LBP histogram of the clutter. The outiler of the detector is classified as the target. In the real-life IPIX radar data sets, our visual feature based detector shows favorable detection rate compared to other three existing approaches.