Heterogeneous Mixture of Experts for Remote Sensing Image Super-Resolution
Bowen Chen, Keyan Chen, Mohan Yang, Zhengxia Zou, Zhenwei Shi

TL;DR
This paper introduces a heterogeneous mixture of experts model with multi-level feature aggregation and dual-routing for remote sensing image super-resolution, significantly improving reconstruction quality over existing methods.
Contribution
It proposes a novel heterogeneous MoE model with specialized experts and a dual-routing mechanism tailored for complex remote sensing images.
Findings
Achieves superior SR accuracy on UCMerced and AID datasets.
Outperforms state-of-the-art super-resolution methods.
Demonstrates effective handling of diverse ground object details.
Abstract
Remote sensing image super-resolution (SR) aims to reconstruct high-resolution remote sensing images from low-resolution inputs, thereby addressing limitations imposed by sensors and imaging conditions. However, the inherent characteristics of remote sensing images, including diverse ground object types and complex details, pose significant challenges to achieving high-quality reconstruction. Existing methods typically employ a uniform structure to process various types of ground objects without distinction, making it difficult to adapt to the complex characteristics of remote sensing images. To address this issue, we introduce a Mixture of Experts (MoE) model and design a set of heterogeneous experts. These experts are organized into multiple expert groups, where experts within each group are homogeneous while being heterogeneous across groups. This design ensures that specialized…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGeochemistry and Geologic Mapping · Atmospheric and Environmental Gas Dynamics · Advanced Technologies in Various Fields
MethodsSparse Evolutionary Training
