为了改进微地形滑坡遥感影像分类技术,从而提高微地形滑坡遥感信息提取的精度,采用湖北宜昌部分地区的无人机航拍高光谱影像(HSI)和激光雷达(LiDAR)数据作为研究数据源,并对高光谱和LiDAR数据进行融合,最后采用结合注意力模块(CBAM)的卷积神经网络(CNN)方法,对融合后的数据进行滑坡信息提取。研究表明,利用高光谱和雷达数据的优势,可以更准确地提取滑坡信息。
Abstract
The aim of this research is to enhance the extraction accuracy by improving the classification of micro-terrain landslide remote sensing information. The landslide information in local areas of Yichang was extracted by using the method of Convolutional Neural Networks (CNN) combined with Convolutional Block Attention Module (CBAM) based on the fusion of Unmanned Aerial Vehicle (UAV) hyperspectral image (HSI) and Light Detection and Ranging (LiDAR) data. Results demonstrated that landslide information can be extracted with more accuracy based on the advantages of hyperspectral and radar data.
关键词
高光谱影像 /
激光雷达 /
数据融合 /
注意力模块 /
滑坡信息提取
Key words
hyperspectral image /
LiDAR /
data fusion /
CBAM /
landslide information extraction
中图分类号：
P642.22
TP75
TP183
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] DING A, ZHANG Q, ZHOU X, et al. Automatic Recognition of Landslide Based on CNN and Texture Change Detection[C]∥Proceedings of the 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), IEEE. doi: 10.1109/YAC40241.2016.
[2] GHORBANZADEH O, BLASCHKE T, GHOLAMNIA K, et al. Evaluation of Different Machine Learning Methods and Deep-learning Convolutional Neural Networks for Landslide Detection[C]∥doi: 10.3390/rs11020196.
[3] 杨思睿,薛朝辉,张 玲,等.高光谱与LiDAR数据融合研究:以黑河中游张掖绿洲农业区精细作物分类为例[J].国土资源遥感,2018,30(4):33-40.
[4] 田 冀,孙 虎,王 彬,等.无人机航空摄影技术在公路建设引起的滑坡监测的应用[J].科研信息化技术与应用,2017,8(3):44-50.
[5] 李 尧.基于深度学习的滑坡检测算法研究[D].成都:成都理工大学,2018.
[6] 徐苗苗.基于高光谱与机载LiDAR融合的地物分类方法研究[D].哈尔滨:东北林业大学,2020.
[7] 李 波.机载激光点云和高光谱影像融合的城市地物分类研究[D].武汉:武汉大学,2017.
[8] 张蒙蒙.基于深度学习的高光谱图像融合分类技术研究[D].北京:北京化工大学,2019.
[9] BIGDELI B, SAMADZADEGAN F, REINARTZ P. Fusion of Hyperspectral and LiDAR Data Using Decision Template-Based Fuzzy Multiple Classifier System[J]. IEEE Journal of Applied Earth Observation and Geoinformation, 2015, 38(1): 309-320.
[10]ZHANG Y, YANG H L, PRASAD S,et al. Ensemble Multiple Kernel Active Learning for Classification of Multisource Remote Sensing Data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(2): 845-858.
[11]刘清旺,谭炳香,胡凯龙,等.机载激光雷达和高光谱组合系统的亚热带森林估测遥感试验[J].高技术通讯,2016,26(3):264-274.
[12]PEDERGNANA M, MARPU P R, MURA M D, et al. Classification of Remote Sensing Optical and LiDAR Data Using Extended Attribute Profiles[J]. IEEE Journal of Selected Topics in Signal Processing, 2012, 6(7): 856-865.
[13]聂 晶.基于LiDAR点云数据的滑坡灾害研究[D].南昌:东华理工大学,2017.
[14]NI L, GAO L R, LI S S,et al.Edge-constrained Markov Random Field Classification by Integrating Hyperspectral Image with LiDAR Data Overurban Areas[J]. Journal of Applied Remote Sensing, 2014, 8(1): 205-207.
基金
国家电网湖北省电力有限公司科技项目(52152018002S)
PDF(4109 KB)
