SAR Ship Instance Segmentation with Dynamic Key Points Information Enhancement

Abstract

There  are  several  unresolved  issues  in  the  field  ofship  instance  segmentation  in  synthetic  aperture  radar  (SAR)images.  Firstly,  in  inshore  dense  ship  area,  the  problems  ofmissed  detections  and  mask  overlap  frequently  occur.  Secondly,in   inshore   scenes,   false   alarms   occur   due   to   strong   clutterinterference.  In  order  to  address  these  issues,  we  propose  anovel  ship  instance  segmentation  network  based  on  dynamickey  points  information  enhancement.  In  the  detection  branchof   the   network,   a   dynamic   key   points   module   (DKPM)   isdesigned  to  incorporate  the  target’s  geometric  information  intothe parameters of the dynamic mask head using implicit encodingtechnique.   Additionally,   we   introduce   a   dynamic   key   pointsencoding  branch,  which  encodes  the  target’s  strong  scatteringregions as dynamic key points. It strengthens the network’s abilityto  learn  the  correspondence  between  local  regions  with  strongscattering  and  overall  ship  targets,  effectively  mitigating  maskoverlap issues. Moreover, it enhances the discriminative ability ofnetwork between ship targets and clutter interference, leading toa reduction in false alarm rates. To further enhance the dynamickey  points  information,  a  instance-wise  attention  map  module(IAMM)  is  designed,  which  decodes  the  key  points  during  themask prediction period, generating instance-wise attention mapsbased  on  two-dimensional  Gaussian  distribution.  This  modulefurther enhances the sensibility of network to specific instances.Simulation experiments conducted on the Polygon SegmentationSAR  Ship  Detection  Dataset  (PSeg-SSDD)  and  High  ResolutionSAR Images Dataset (HRSID) demonstrate the superiority of ourproposed  method  over  other  state-of-the-art  methods  in  inshoreand offshore scenes.</p