文章基本信息

标题：U-Net: A Smart Application with Multidimensional Attention Network for Remote Sensing Images
本地全文：下载
作者：Yao Wang ; Jiayuan Kong ; Hesheng Zhang 等
期刊名称：Scientific Programming
印刷版ISSN：1058-9244
出版年度：2022
卷号：2022
DOI：10.1155/2022/1603273
语种：English
出版社：Hindawi Publishing Corporation
摘要：Building segmentation is an important step in urban planning and development. In this work, we propose a new deep learning model, namely Multidimension Attention U-Net (MDAU-Net), to accurately segment building pixels and nonbuilding pixels in remote sensing images. Furthermore, we introduce a novel Multidimension Modified Efficient Channel Attention (MD-MECA) model to enhance the network discriminative ability through considering the interdependence between feature maps. Through deepening the U-Net model to a seven-story structure, the ability to identify the building is enhanced. We apply MD-MECA to the “skip connections” in traditional U-Net, instead of simply copying the feature mapping of the contraction path to the matching extension path, to optimize the feature transfer more efficiently. The obtained results show that our proposed MDAU-Net framework achieves the most advanced performance on publicly available building data sets (i.e. the precision over the Massachusetts buildings data set and WHU data set are 97.04% and 95.68%, respectively). Furthermore, we observed that the proposed framework outperforms several state-of-the-art approaches.