A deep deformable residual learning network for SAR images segmentation

TL;DR

This paper introduces a deep deformable residual learning network designed for precise SAR image target segmentation, leveraging deformable convolutions and residual blocks to better preserve target contours and geometric details.

Contribution

The paper proposes a novel deep learning architecture combining deformable convolutions and residual learning for improved SAR target segmentation accuracy.

Findings

Outperforms traditional methods like SR and CFAR in segmentation accuracy

Effectively preserves target contours and geometric information

Demonstrates superior performance on MSTAR dataset

Abstract

Reliable automatic target segmentation in Synthetic Aperture Radar (SAR) imagery has played an important role in the SAR fields. Different from the traditional methods, Spectral Residual (SR) and CFAR detector, with the recent adavance in machine learning theory, there has emerged a novel method for SAR target segmentation, based on the deep learning networks. In this paper, we proposed a deep deformable residual learning network for target segmentation that attempts to preserve the precise contour of the target. For this, the deformable convolutional layers and residual learning block are applied, which could extract and preserve the geometric information of the targets as much as possible. Based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set, experimental results have shown the superiority of the proposed network for the precise targets segmentation.