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A Novel Framework for Remote Sensing Image Synthesis with Optimal Transport.

Jinlong He¹, Xia Yuan^1,2, Yong Kou¹

¹School of Computer Science, Sichuan University, Chengdu 610065, China.

Sensors (Basel, Switzerland)

|April 28, 2025

Summary

This study introduces a Generative Adversarial Network (GAN) for synthesizing remote sensing images (RSIs). Novel attention modules and optimal transport enhance image realism by addressing intra- and inter-class variance challenges.

Keywords:

generative adversarial networks optimal transport remote sensing images

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Area of Science:

Computer Vision
Remote Sensing
Artificial Intelligence

Background:

Remote sensing image synthesis faces challenges due to large intraclass and small interclass variance.
Existing methods struggle to generate realistic and distinguishable remote sensing images (RSIs).

Purpose of the Study:

To develop an advanced Generative Adversarial Network (GAN) for high-quality remote sensing image synthesis.
To address the challenges of intraclass variance and interclass variance in RSIs.

Main Methods:

A GAN-based framework incorporating two novel attention modules.
Attention modules designed to enhance within-class similarity and between-class diversity.
Integration of optimal transport (OT) for approximating human perceptual loss.

Main Results:

The proposed method significantly improves the quality and realism of synthesized RSIs.
Attention mechanisms effectively manage intraclass variance and interclass variance.
Experimental results validate the superiority of the GAN-based approach in remote sensing image synthesis.

Conclusions:

The developed GAN framework with attention modules and OT is effective for remote sensing image synthesis.
This approach offers a significant advancement in generating high-fidelity RSIs.
The method holds promise for various remote sensing applications requiring accurate image generation.