Small Vessel Detection Based on Adaptive Dual-Polarimetric Sar Feature Fusion and Attention-Enhanced Feature Pyramid Network

Abstract

Small vessels in synthetic aperture radar (SAR) images usually have weak scattering intensity and occupy only a few numbers of image pixels, resulting in a high miss detection rate during the detection process. Regarding the problem, two solutions were presented in this paper. Firstly, dual-polarimetric SAR data were used and dual-polarimetric features were adaptively fused. Comparing to single-polarization and conventional non-adaptive fusion method, it optimally enhanced the characteristics of small vessels. Secondly, the conventional feature pyramid network (FPN) was enhanced by reducing the downsampling factor, adding spatial attention, and channel attention. The added spatial attention enhanced the significant features of small vessels on the large-scale feature map; the added channel attention filtered out the spliced features maps that were benefiting small vessel detection and reduced feature redundancy. Experimental results on the small vessel data set of Sentinel-1 verified that it not only reduced the miss detection rate but also improved calculation efficiency.