Semantically Robust Unsupervised Image Translation for Paired Remote Sensing Images

TL;DR

This paper introduces SRUIT, a novel unsupervised image translation method for bi-temporal remote sensing images that ensures semantic consistency and produces deterministic outputs, improving change detection accuracy.

Contribution

The proposed SRUIT method uniquely enforces semantic robustness and determinism in unsupervised translation by sharing high-level network layers and utilizing cross-cycle consistency.

Findings

Achieves semantically consistent translations with high perceptual quality.

Effectively captures land cover changes in bi-temporal images.

Outperforms existing methods in preserving semantic content.

Abstract

Image translation for change detection or classification in bi-temporal remote sensing images is unique. Although it can acquire paired images, it is still unsupervised. Moreover, strict semantic preservation in translation is always needed instead of multimodal outputs. In response to these problems, this paper proposes a new method, SRUIT (Semantically Robust Unsupervised Image-to-image Translation), which ensures semantically robust translation and produces deterministic output. Inspired by previous works, the method explores the underlying characteristics of bi-temporal Remote Sensing images and designs the corresponding networks. Firstly, we assume that bi-temporal Remote Sensing images share the same latent space, for they are always acquired from the same land location. So SRUIT makes the generators share their high-level layers, and this constraint will compel two domain mapping to fall into the same latent space. Secondly, considering land covers of bi-temporal images could evolve into each other, SRUIT exploits the cross-cycle-consistent adversarial networks to translate from one to the other and recover them. Experimental results show that constraints of sharing weights and cross-cycle consistency enable translated images with both good perceptual image quality and semantic preservation for significant differences.