AFLDiff: Adaptive Frequency-Aware Latent Diffusion Model for Road Extraction in Remote Sensing Images

Accurate road extraction from remote sensing images is of great importance, yet the complex backgrounds, significant scale differences, and detailed characteristics of road information pose a tremendous challenge to the semantic modeling capabilities of deep learning methods. Recent studies have shown impressive success with diffusion-based methods in semantic segmentation. However, when applied to road extraction tasks, the remarkable performance can be diminished by intricate terrains and unreasonable feature fusion. In this article, we reframe road extraction as a mask-generating task, progressively refining road mask predictions through a timestep-by-timestep denoising process. Specifically, we propose a novel adaptive frequency-aware latent diffusion framework named AFLDiff. The pretrained VQ-VAE encoder with coupled perceptual compression suppresses semantic features that are weakly associated with roads in remote sensing images, reconstructing the image into a low-dimensional latent space for training. The adaptive frequency-aware module filters out redundant noise from the noisy features and enhances useful high- and low-frequency components, bridging the gap between noisy and semantic features for better fusion. Extensive experimental results demonstrate that our method achieves state-of-the-art (SOTA) performance on the DeepGlobe, Massachusetts, and CHN6-CUG datasets, with cross-dataset validation further confirming its robustness and generalization capabilities.