Multi Frequency Analysis of Scattering Matrix and Scattering Power Matrix for Marine Vessels Detection

Abstract

Independence of Synthetic Aperture Radar (SAR) images from weather and sun illumination helps us in maritime surveillance like an oil spill, to identify the illegal fishery activity and unauthorized marine vessels. Detection of marine vessels is like a point target detection in coarse to moderate resolution images. Detection of point targets is adversely affects by speckle noise. Object detection in SAR images, without explicitly reducing the speckle noise is one of the challenging task. In this paper two algorithms are presented, which show how improvement in the power of point target lead us to reduced number of false alarms. The first algorithm has three parts. First part uses Grave matrix, which is a ( $2\times 2$ ) Hermitian power matrix, generated by the multiplication of Sinclair matrix and conjugate of Sinclair matrix. Second part uses discrimination criteria to discriminate between clutter and vessels based on eigenvalue of the Grave matrix (G). The third part, fill the gaps by using morphological dilation. The only difference between the first and the second algorithms is that, in the second algorithm we uses Sinclair matrix (S2) instead of Grave matrix. Both the algorithms tested on two full-polarization (HH, HV, VH, VV) datasets and the results show the importance of scattering power matrix (G) as compared to scattering matrix (S2) for point target detection. The first data set is of size $498^{\ast}498$ captured by AlOS-1 PALSAR L-band data, covering the coastal region of Singapore. The second data set is of size $472^{\ast}472$ , covering the coast of Vancouver acquired in C-band by Radarsat-2.