September 10, 2025The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesOpen Access

Remote sensing semantic segmentation based on multimodal feature alignment and fusion

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MFAFUNet enhances semantic segmentation performance by effectively aligning features across modalities.
Experiments reveal that the proposed framework outperforms conventional single-modal techniques in remote sensing applications.
The method utilizes Haar wavelet transform for downsampling, resulting in better feature extraction with CNNs and ViTs.
Semantic distribution alignment simplifies the integration of multi-modal information, improving overall context representation.

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Implication

This study demonstrates a robust multi-modal fusion framework for semantic segmentation, highlighting improved feature alignment in remote sensing data.

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Chang et al. (2025) studied this question.

synapsesocial.com/papers/68c23fc3b210217d64797dfd https://doi.org/10.5194/isprs-archives-xlviii-g-2025-1785-2025

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Implication

This study demonstrates a robust multi-modal fusion framework for semantic segmentation, highlighting improved feature alignment in remote sensing data.

Remote sensing semantic segmentation based on multimodal feature alignment and fusion

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Remote sensing semantic segmentation based on multimodal feature alignment and fusion

Key Points

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Implication

Cite This Study

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