Heterogeneous Mixture of Experts for Remote Sensing Image Super-Resolution

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 activation parameters can be employed to handle the diverse and intricate details of ground objects effectively. To better accommodate the heterogeneous experts, we propose a multi-level feature aggregation strategy to guide the routing process. Additionally, we develop a dual-routing mechanism to adaptively select the optimal expert for each pixel. Experiments conducted on the UCMerced and AID datasets demonstrate that our proposed method achieves superior SR reconstruction accuracy compared to state-of-the-art methods. The code will be available at https://github.com/Mr-Bamboo/MFG-HMoE.