An improved U-shape Kolmogorov-Arnold network for bridge detection of remote sensing images

Le résumé fourni par la source

Accurate bridge detection in remote sensing images is crucial for water resource management, especially in flood-prone areas. However, traditional deep learning models such as U-Net struggle to effectively identify bridges due to their diverse morphologies, small size relative to the overall image, and the high-resolution nature of remote sensing imagery. This study presents an improved U-KAN model, which integrates the Kolmogorov-Arnold Network (KAN) with an enhanced U-Net framework and introduces a channel attention mechanism using the Squeeze-and-Excitation (SE) module. The proposed model addresses the challenges of class imbalance, boundary ambiguity, and multi-scale variability by incorporating KANLinear layers and replacing ReLU activations with LeakyReLU. Extensive experiments on a bridge dataset demonstrate that the proposed algorithm outperforms state-of-the-art models, achieving a higher Intersection over Union (IoU), precision, and recall. Comparative and ablation studies further validate the effectiveness of the proposed architectural modifications. This improved U-KAN provides a robust and efficient solution for automatic bridge extraction from remote sensing images, offering valuable support for water resource management applications.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.