Open AccessMulti-Feature High-Resolution Remote Sensing Road Extraction Based on Computer Convolutional Neural Network
Author(s) -
Jianhua Zhu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1578/1/012072
Subject(s) - computer science , convolutional neural network , process (computing) , remote sensing , feature extraction , feature (linguistics) , remote control , image resolution , artificial intelligence , real time computing , computer hardware , geography , linguistics , philosophy , operating system
As an important facility to improve people’s material living standards, the accuracy of its mapping work directly determines the quality of people’s production and life at this stage. However, in the actual process of mapping, the application effect of remote sensing image technology has not been fully reflected, which is closely related to the content of the remote sensing technology application process. In order to control the difficulty of its auxiliary mapping work, relevant personnel should analyze the value effect of remote sensing technology in practice, so that the mapping work can fully realize the advantages of efficiency improvement and shortened working time brought by the use of remote sensing image technology. In this way, the application of remote sensing image technology can be fully exerted during the development of map mapping. This paper analyzes the extraction of multi-feature high-resolution remote sensing roads.