MFFNet: a wavelet transform-based multimodal frequency fusion network for remote sensing semantic segmentation

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MFFNet achieves improved segmentation performance, outperforming traditional methods with a mean Intersection over Union (mIoU) of 84.21% on the Vaihingen dataset.
Key metrics for MFFNet include overall accuracies of 92.26% and 91.16% on the Vaihingen and Potsdam datasets, respectively, illustrating its effectiveness.
The method employs a wavelet transform-based approach within its spatial-frequency domain wavelet attention fusion module, optimizing feature extraction.
Results indicate that MFFNet effectively combines data from various sensors, highlighting its potential for advancements in remote sensing applications.

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Proposed MFFNet enhances semantic segmentation accuracy in remote sensing, integrating wavelet-based features, improving data fusion.

Li et al. (2025) studied this question.

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