计算机科学
全色胶片
稳健性(进化)
人工智能
特征提取
分割
基本事实
深度学习
背景(考古学)
信息抽取
计算机视觉
模式识别(心理学)
数据挖掘
图像分辨率
遥感
古生物学
生物化学
化学
生物
基因
地质学
作者
Yang Du,Qinghong Sheng,Weili Zhang,Chaozhe Zhu,Jun Li,Bo Wang
出处
期刊:Isprs Journal of Photogrammetry and Remote Sensing
日期:2023-09-01
卷期号:203: 230-245
标识
DOI:10.1016/j.isprsjprs.2023.07.026
摘要
The use of artificial intelligence has led to an increase in road extraction projects from satellite images through deep learning. However, multi-spectral images (MSI) have been largely overlooked in road extraction algorithms due to their lower resolution compared to panchromatic or fused images. Additionally, deep learning faces the challenge of image content reasoning from distant contexts in data rule mining. To address these issues, we propose a new method for road extraction called the MSI-guided Segmentation Network, which utilizes all the data from the GF2 satellite to achieve optimal results. This study highlights the advantages of using MSI with low-resolution for obtaining deeper semantic information in a faster manner, while high-resolution fused images are better suited for extracting precise characteristics. The proposed method includes two sections: (1) a local symmetry feature fusion to enhance the network's local context-awareness for shallow details, and (2) a global asymmetric semantic fusion to improve the network's capability to comprehend the whole scene for deep semantic information. Moreover, to evaluate the robustness and generalization of this method, we have provided a GF2 Full-band China Road Dataset. The codes and datasets will be made public on https://github.com/Dudujia160918/MSNet.
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