A Dual-fusion Semantic Segmentation Framework With GAN For SAR Images

TL;DR

This paper introduces a dual-fusion framework combining GAN-generated optical images with SAR images to enhance semantic segmentation accuracy in remote sensing, leveraging enriched data representations.

Contribution

It proposes a novel dual-fusion segmentation network that integrates GAN-generated optical images with SAR data, improving segmentation robustness and accuracy.

Findings

Enhanced segmentation performance compared to existing methods

Effective use of GAN-generated optical images for SAR data augmentation

Improved object representation through attention modules

Abstract

Deep learning based semantic segmentation is one of the popular methods in remote sensing image segmentation. In this paper, a network based on the widely used encoderdecoder architecture is proposed to accomplish the synthetic aperture radar (SAR) images segmentation. With the better representation capability of optical images, we propose to enrich SAR images with generated optical images via the generative adversative network (GAN) trained by numerous SAR and optical images. These optical images can be used as expansions of original SAR images, thus ensuring robust result of segmentation. Then the optical images generated by the GAN are stitched together with the corresponding real images. An attention module following the stitched data is used to strengthen the representation of the objects. Experiments indicate that our method is efficient compared to other commonly used methods