Journal Article

FEMNet: A Feature-Enriched Mamba Network for Cloud Detection in Remote Sensing Imagery

Nie, Han, Liu, Weixing, Luo, Bin, Liu, Jun, Su, Xin

2025

Overview

Accurate and efficient cloud detection is critical for maintaining the usability of optical remote sensing imagery, particularly in large-scale Earth observation systems. In this study, we propose FEMNet, a lightweight dual-branch network that combines state space modeling with convolutional encoding for multi-class cloud segmentation. The Mamba-based encoder captures long-range semantic dependencies with linear complexity, while a parallel CNN path preserves spatial detail. To address the semantic inconsistency across feature hierarchies and limited context perception in decoding, we introduce the following two targeted modules: a cross-stage semantic enhancement (CSSE) block that adaptively aligns low- and high-level features, and a multi-scale context aggregation (MSCA) block that integrates contextual cues at multiple resolutions. Extensive experiments on five benchmark datasets demonstrate that FEMNet achieves state-of-the-art performance across both binary and multi-class settings, while requiring only 4.4M parameters and 1.3G multiply–accumulate operations. These results highlight FEMNet’s suitability for resource-efficient deployment in real-world remote sensing applications.

• Publisher: MDPI AG
• Subject: cloud detection / Clouds / deep learning / Design / Efficiency / Hierarchies / Mamba / Real time / Remote sensing / remote sensing image / Semantics / State space models