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Remote Sensing
Volume 17
Issue 24
10.3390/rs17244028
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

RFANSR: Receptive Field Aggregation Network for Lightweight Remote Sensing Image Super-Resolution

by
Xiaoyu Yan
1,
Wei Song
1,
Xiaotong Feng
1,
Wei Guo
2 and
Keqing Ning
1,*
1
School of Artificial Intelligence and Computer Science, North China University of Technology, Beijing 100144, China
2
School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(24), 4028; https://doi.org/10.3390/rs17244028 (registering DOI)
Submission received: 18 November 2025 / Revised: 9 December 2025 / Accepted: 10 December 2025 / Published: 14 December 2025
(This article belongs to the Section Remote Sensing Image Processing)
Versions Notes

Abstract

Expanding the receptive field while maintaining efficiency is a key challenge in lightweight remote sensing super-resolution. Existing methods often suffer from parameter redundancy or insufficient channel utilization. To address these issues, we propose the Receptive Field Aggregation Network (RFANSR). First, we design a Progressive Receptive Field Aggregator (PRFA). It expands the receptive field by cascading medium-sized kernels, avoiding the heavy overhead of extremely large kernels. Second, we introduce a Statistical Guidance Module (SGM). This module replaces inefficient identity mappings with statistical channel recalibration to maximize feature utility. Additionally, we propose a Spatial-Gated Feed-Forward Network (SGFN) to reduce information loss via spatial attention. Extensive experiments demonstrate that RFANSR outperforms state-of-the-art lightweight models. Notably, RFANSR achieves PSNR improvements of 0.06 dB on RSCNN7 and 0.14 dB on DOTA datasets. Remarkably, it requires only 383 K parameters, representing a 45.4% reduction compared to DLKN.
Keywords: remote sensing image super-resolution; lightweight neural network; CNN; receptive field aggregation remote sensing image super-resolution; lightweight neural network; CNN; receptive field aggregation

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MDPI and ACS Style

Yan, X.; Song, W.; Feng, X.; Guo, W.; Ning, K. RFANSR: Receptive Field Aggregation Network for Lightweight Remote Sensing Image Super-Resolution. Remote Sens. 2025, 17, 4028. https://doi.org/10.3390/rs17244028

AMA Style

Yan X, Song W, Feng X, Guo W, Ning K. RFANSR: Receptive Field Aggregation Network for Lightweight Remote Sensing Image Super-Resolution. Remote Sensing. 2025; 17(24):4028. https://doi.org/10.3390/rs17244028

Chicago/Turabian Style

Yan, Xiaoyu, Wei Song, Xiaotong Feng, Wei Guo, and Keqing Ning. 2025. "RFANSR: Receptive Field Aggregation Network for Lightweight Remote Sensing Image Super-Resolution" Remote Sensing 17, no. 24: 4028. https://doi.org/10.3390/rs17244028

APA Style

Yan, X., Song, W., Feng, X., Guo, W., & Ning, K. (2025). RFANSR: Receptive Field Aggregation Network for Lightweight Remote Sensing Image Super-Resolution. Remote Sensing, 17(24), 4028. https://doi.org/10.3390/rs17244028

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