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Diff-HRNet: A Diffusion Model-Based High-Resolution Network for Remote Sensing Semantic Segmentation
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DOI：10.1109/LGRS.2024.3505552.png)
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Abstract
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The semantic segmentation methods based on deep neural networks predominantly employ supervised learning, relying heavily on the quantity and quality of annotated samples. Due to the complexity of high-resolution remote sensing imagery, obtaining sufficient and precise pixel-level labeled data is highly challenging. This letter introduces a novel self-supervised learning method using a pretrained denoising diffusion probabilistic model (DDPM) to leverage semantic information from large-scale unlabeled remote sensing imageries. Building on this, a multistage fusion scheme between pretrained features and high-resolution features is proposed, enabling the network to learn more effective strategies to leverage prior information provided by the pretrained model while preserving the rich semantic details of high-resolution images. Experimental results on two remote sensing semantic segmentation datasets show that the proposed Diff-HRNet outperforms all compared methods, demonstrating the potential of pretrained diffusion models in extracting crucial feature representations for semantic segmentation tasks.
Keywords:
Feature extraction
Remote sensing
Semantics
Spatial resolution
Diffusion models
Noise reduction
Noise measurement
Convolution
Supervised learning
Denoising diffusion probabilistic model (DDPM)
self-supervised learning
semantic segmentation
semantic segmentation
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