September 10, 2025Remote SensingOpen Access

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

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FEMNet achieves state-of-the-art performance for cloud detection in remote sensing imagery, ensuring usability in Earth observation systems.
With only 4.4 million parameters, FEMNet combines dual-branch networks to effectively process multi-class cloud segmentation tasks.
The unique cross-stage semantic enhancement and multi-scale context aggregation modules improve feature alignment and context perception.
Extensive experiments show FEMNet's efficiency and effectiveness across various benchmark datasets, demonstrating its practical deployment potential.

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Implication

This study proposes FEMNet, which enhances cloud detection in remote sensing imagery, highlighting its advanced semantic enhancement and context aggregation capabilities.

Cite This Study

Liu et al. (2025) studied this question.

synapsesocial.com/papers/68c23e02b210217d64790620 https://doi.org/10.3390/rs17152639

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Implication

This study proposes FEMNet, which enhances cloud detection in remote sensing imagery, highlighting its advanced semantic enhancement and context aggregation capabilities.

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

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Discussion

Implication

Cite This Study

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

Key Points

Discussion

Implication

Cite This Study