Accurate road extraction from high-resolution remote sensing imagery plays a vital role in numerous geospatial applications, including urban planning, disaster emergency response, intelligent transportation, and map updating. However, significant variations in road width, geometry, and orientation, together with complex backgrounds such as shadows, vegetation, and occlusions, often lead to incomplete extraction and poor structural continuity. To address these challenges, this paper proposes an adaptive scale-aware road extraction network, termed ASAR-Net, which jointly improves multi-scale feature representation and structural continuity. Specifically, an Adaptive Bidirectional Enhancement Module (ABEM) is introduced in the encoder to improve the representation of roads with diverse spatial scales through adaptive scale-aware convolution and bidirectional attention. Furthermore, a Directional Fusion Module (DFM) is incorporated into the decoder to guide feature reconstruction along road orientations using dynamic snake convolution, thereby facilitating the recovery of continuous and complete road structures. Extensive experiments on two public benchmark datasets, Massachusetts Roads and DeepGlobe, demonstrate that ASAR-Net consistently outperforms several representative state-of-the-art road extraction methods in terms of mIoU and F1-score. The proposed network effectively improves both the semantic completeness and structural continuity of extracted road networks, demonstrating its robustness and effectiveness for road extraction in complex high-resolution remote sensing scenarios.
Xiaotong Guo, Guang Yang, Yue-bao Wang et al.· Applied Sciences· 0 citations
Remote sensing image super-resolution (SR) aims to enhance spatial resolution and recover image details, which typically enhances the quality of optical remote sensing imagery. However, interference from cloud haze cover and sensor noise often leads to distorted details and artifacts in reconstructed images of conventional deep learning SR approaches, significantly limiting reconstruction fidelity. To address these challenges, we propose a novel SR framework based on the flow matching paradigm and a diffusion transformer, named FlowT-SR, which achieves superior and reliable reconstruction quality by jointly mitigating sensor noise and thin cloud interference. First, an evolution path from low-resolution images to ground-truth images is constructed based on the optimal transport displacement interpolation mechanism, and the corresponding vector field that governs this evolution is employed as the supervision signal for subsequent model training. Then, a multi-scale interference suppression (MSIS) module is combined with a novel diffusion transformer network (DiTNet) to predict the vector field. The MSIS module performs preliminary denoising and captures the spatial distribution of thin cloud and haze in low-resolution images, providing degradation-aware feature representations for DiTNet. Subsequently, a DiTNet is presented to predict the evolution vector field obtained in the first stage, which consists of ten layers based on the diffusion transformer. By accurately predicting the vector field at any time step, the model effectively reduces the impact of cloud haze and noise interference to improve the reconstruction precision. Finally, driven by the predicted vector field along the evolution path, the SR remote sensing image is generated through solving the corresponding ordinary differential equation, yielding cloud-free and noise-reduced results. Extensive experiments on our dataset and the public CUHK Cloud Removal dataset demonstrate that FlowT-SR effectively suppresses cloud haze and noise interference, achieving superior reconstruction performance compared with current state-of-the-art methods in terms of both PSNR and SSIM.
Yu-Tong Zhang, Guang Yang, Rong Liu et al.· Italian National Conference...· 0 citations