Multichannel InSAR elevation reconstruction method based on dual-stream network

仰角(弹道) 计算机科学 人工智能 干涉合成孔径雷达 地形 数字高程模型 计算机视觉 解码方法 合并(版本控制) 遥感 模式识别(心理学) 合成孔径雷达 地质学 算法 地图学 数学 地理 几何学 情报检索
作者
Xianming Xie,Geng Dianqiang,Hou Guozheng,Qingning Zeng,Zheng Zhan-heng
出处
期刊:Optics and Lasers in Engineering [Elsevier BV]
卷期号:172: 107874-107874 被引量:1
标识
DOI:10.1016/j.optlaseng.2023.107874
摘要

This paper presents a multichannel InSAR elevation reconstruction method based on deep learning, where a dual-stream network consisting of an elevation reconstruction stream and a boundary detection stream, named as ERSBDS, is built to reconstruct elevation maps for observed terrains, from multiple interferograms. First, the elevation reconstruction stream adopts a modified DeepLabV3+ architecture, in which the Xception network is replaced by the lightweight network called MobileNetV3 in the encoder for not only reducing the network parameters but also maintaining the performance of the network, and then a spatial attention module is added to the encoding and decoding path to enhance the network's attention to the spatial information of feature maps. Second, the boundary detection stream is mainly composed of residual blocks, which can detect the boundary information of observed terrains and merge it into the elevation reconstruction stream to improve the accuracy of elevation reconstruction for observed scenes. Finally, a suitable data set is constructed to enable the trained network to accurately reconstruct elevation maps for observed scenes. The experiments for multichannel InSAR elevation reconstruction for observed scenes demonstrate the effectiveness of the proposed method, and show the advantages of this method in the accuracy and efficiency of elevation reconstruction, compared with some of the most commonly used methods.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
wztin发布了新的文献求助10
1秒前
水濑心源发布了新的文献求助10
1秒前
FashionBoy应助你快睡吧采纳,获得10
2秒前
3秒前
pluto应助开放雪碧采纳,获得10
3秒前
3秒前
1351567822应助Whim采纳,获得20
3秒前
大方悟空发布了新的文献求助10
3秒前
elgar612发布了新的文献求助10
4秒前
4秒前
田様应助小蒋采纳,获得10
5秒前
6秒前
6秒前
victor完成签到,获得积分10
6秒前
英俊的铭应助追寻的绮露采纳,获得10
7秒前
HJJ发布了新的文献求助10
8秒前
南与枝完成签到 ,获得积分10
11秒前
水濑心源发布了新的文献求助30
12秒前
13秒前
Liufgui应助舒适的士萧采纳,获得10
13秒前
111111111发布了新的文献求助10
13秒前
14秒前
你快睡吧完成签到,获得积分10
14秒前
hua应助lm采纳,获得10
14秒前
橘子海发布了新的文献求助10
14秒前
15秒前
朱博完成签到,获得积分10
15秒前
消消消消气完成签到 ,获得积分10
15秒前
风趣惜霜完成签到,获得积分10
15秒前
16秒前
17秒前
17秒前
cbbb发布了新的文献求助10
18秒前
20秒前
20秒前
彭于晏应助111111111采纳,获得10
20秒前
22秒前
星辰大海应助郭小宝采纳,获得10
22秒前
万能图书馆应助倪小呆采纳,获得10
22秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3988827
求助须知:如何正确求助?哪些是违规求助? 3531183
关于积分的说明 11252671
捐赠科研通 3269809
什么是DOI,文献DOI怎么找? 1804780
邀请新用户注册赠送积分活动 881885
科研通“疑难数据库(出版商)”最低求助积分说明 809021