Remote sensing change detection model based on multi⁃scale fusion

被引：0
|
作者：
Li X.-F. [1 ]
Song Z.-X. [1 ]
Zhu R. [1 ]
Zhang X.-L. [1 ]
机构：
[1] College of Computer Science and Technology, Jilin University, Changchun
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2024年 / 54卷 / 02期
关键词：
change detection; deep learning; multi-scale fusion; remote sensing image;
D O I：
10.13229/j.cnki.jdxbgxb.20221340
中图分类号：
学科分类号：
摘要：
The remote sensing image change detection model which is based on multi-scale fusion is proposed to accurately identify the change region of the bi-temporal remote sensing images. First，a multi-scale input pyramid is constructed in the feature extraction stage to receive multi-layer receptive fields and enhance the perception of all information. Then，in order to make a tradeoff between locating the changing area and mining details，the multi-scale calculation is carried out for deep difference features. Finally，the semantic change information can be identified and retained to a great extent by integrating the different feature results of the network. The experimental results show that the proposed model has good performance in both subjective evaluation and objective indexes. © 2024 Editorial Board of Jilin University. All rights reserved.
引用
收藏
页码：516 / 523
页数：7
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