Integrated Airborne LiDAR Data and Imagery for Suburban Land Cover Classification Using Machine Learning Methods
It is valuable to study the land use/land cover (LULC) classification for suburbs. The fusion of Light Detection and Ranging (LiDAR) data and aerial imagery is often regarded as an effective method for the LULC classification; however, more in-depth analysis would be required to explore effective in...
Main Authors: | , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-04-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/19/9/1996 |
id |
doaj-8f1bb78ccbe646d5bd66a5eb74867244 |
---|---|
record_format |
Article |
spelling |
doaj-8f1bb78ccbe646d5bd66a5eb748672442020-11-25T01:36:36ZengMDPI AGSensors1424-82202019-04-01199199610.3390/s19091996s19091996Integrated Airborne LiDAR Data and Imagery for Suburban Land Cover Classification Using Machine Learning MethodsYou Mo0Ruofei Zhong1Haili Sun2Qiong Wu3Liming Du4Yuxin Geng5Shisong Cao6Beijing Advanced Innovation Center for Imaging Theory and Technology, Key Lab of 3D Information Acquisition and Application, MOE, Capital Normal University, Beijing 100048, ChinaBeijing Advanced Innovation Center for Imaging Theory and Technology, Key Lab of 3D Information Acquisition and Application, MOE, Capital Normal University, Beijing 100048, ChinaBeijing Advanced Innovation Center for Imaging Theory and Technology, Key Lab of 3D Information Acquisition and Application, MOE, Capital Normal University, Beijing 100048, ChinaBeijing Advanced Innovation Center for Imaging Theory and Technology, Key Lab of 3D Information Acquisition and Application, MOE, Capital Normal University, Beijing 100048, ChinaBeijing Advanced Innovation Center for Imaging Theory and Technology, Key Lab of 3D Information Acquisition and Application, MOE, Capital Normal University, Beijing 100048, ChinaBeijing Advanced Innovation Center for Imaging Theory and Technology, Key Lab of 3D Information Acquisition and Application, MOE, Capital Normal University, Beijing 100048, ChinaBeijing Advanced Innovation Center for Imaging Theory and Technology, Key Lab of 3D Information Acquisition and Application, MOE, Capital Normal University, Beijing 100048, ChinaIt is valuable to study the land use/land cover (LULC) classification for suburbs. The fusion of Light Detection and Ranging (LiDAR) data and aerial imagery is often regarded as an effective method for the LULC classification; however, more in-depth analysis would be required to explore effective information for enhancing the suburban LULC classification. In this study, first, both aerial imageries and point clouds were simultaneously collected. Then, LiDAR-derived models, i.e., normalized digital surface model (nDSM) and surface intensity model (IM), were generated from the elevation and intensity of point clouds. Further, considering the surface characteristics of ground objects in suburb, we proposed a new LiDAR-derived model, namely surface roughness model (RM), to reveal the degree of surface fluctuations. Additionally, various combinations of aerial imageries and the LiDAR-derived data were used to analyze the effects of multi-variable fusion under different scenarios and optimize the multi-variable integration for suburban LULC classification. The mean decrease impurity method was used to identify the importance of variables; three machine learning classifiers, i.e., random forest (RF), k-nearest neighbor (KNN) and artificial neural network (ANN) were adopted in various scenarios. The results were as follows. The fusion of aerial imagery and all the LiDAR-derived models, i.e., nDSM, RM and IM, with RF classifier performs best in the suburban LULC classification (overall accuracy = 84.75%, kappa coefficient = 0.80). Variable importance analysis shows that nDSM has the highest variable importance proportion (VIP) value, followed by RM, IM, and spectral information, indicating the feasibility of this proposed LiDAR-derived model-RM. This research presents effective methods relating to the application of aerial imagery and LiDAR-derived model for the complex suburban surface scenarios.https://www.mdpi.com/1424-8220/19/9/1996suburban land cover classificationLiDARaerial imagerymachine learningnDSMsurface roughnesssurface intensity |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
You Mo Ruofei Zhong Haili Sun Qiong Wu Liming Du Yuxin Geng Shisong Cao |
spellingShingle |
You Mo Ruofei Zhong Haili Sun Qiong Wu Liming Du Yuxin Geng Shisong Cao Integrated Airborne LiDAR Data and Imagery for Suburban Land Cover Classification Using Machine Learning Methods Sensors suburban land cover classification LiDAR aerial imagery machine learning nDSM surface roughness surface intensity |
author_facet |
You Mo Ruofei Zhong Haili Sun Qiong Wu Liming Du Yuxin Geng Shisong Cao |
author_sort |
You Mo |
title |
Integrated Airborne LiDAR Data and Imagery for Suburban Land Cover Classification Using Machine Learning Methods |
title_short |
Integrated Airborne LiDAR Data and Imagery for Suburban Land Cover Classification Using Machine Learning Methods |
title_full |
Integrated Airborne LiDAR Data and Imagery for Suburban Land Cover Classification Using Machine Learning Methods |
title_fullStr |
Integrated Airborne LiDAR Data and Imagery for Suburban Land Cover Classification Using Machine Learning Methods |
title_full_unstemmed |
Integrated Airborne LiDAR Data and Imagery for Suburban Land Cover Classification Using Machine Learning Methods |
title_sort |
integrated airborne lidar data and imagery for suburban land cover classification using machine learning methods |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2019-04-01 |
description |
It is valuable to study the land use/land cover (LULC) classification for suburbs. The fusion of Light Detection and Ranging (LiDAR) data and aerial imagery is often regarded as an effective method for the LULC classification; however, more in-depth analysis would be required to explore effective information for enhancing the suburban LULC classification. In this study, first, both aerial imageries and point clouds were simultaneously collected. Then, LiDAR-derived models, i.e., normalized digital surface model (nDSM) and surface intensity model (IM), were generated from the elevation and intensity of point clouds. Further, considering the surface characteristics of ground objects in suburb, we proposed a new LiDAR-derived model, namely surface roughness model (RM), to reveal the degree of surface fluctuations. Additionally, various combinations of aerial imageries and the LiDAR-derived data were used to analyze the effects of multi-variable fusion under different scenarios and optimize the multi-variable integration for suburban LULC classification. The mean decrease impurity method was used to identify the importance of variables; three machine learning classifiers, i.e., random forest (RF), k-nearest neighbor (KNN) and artificial neural network (ANN) were adopted in various scenarios. The results were as follows. The fusion of aerial imagery and all the LiDAR-derived models, i.e., nDSM, RM and IM, with RF classifier performs best in the suburban LULC classification (overall accuracy = 84.75%, kappa coefficient = 0.80). Variable importance analysis shows that nDSM has the highest variable importance proportion (VIP) value, followed by RM, IM, and spectral information, indicating the feasibility of this proposed LiDAR-derived model-RM. This research presents effective methods relating to the application of aerial imagery and LiDAR-derived model for the complex suburban surface scenarios. |
topic |
suburban land cover classification LiDAR aerial imagery machine learning nDSM surface roughness surface intensity |
url |
https://www.mdpi.com/1424-8220/19/9/1996 |
work_keys_str_mv |
AT youmo integratedairbornelidardataandimageryforsuburbanlandcoverclassificationusingmachinelearningmethods AT ruofeizhong integratedairbornelidardataandimageryforsuburbanlandcoverclassificationusingmachinelearningmethods AT hailisun integratedairbornelidardataandimageryforsuburbanlandcoverclassificationusingmachinelearningmethods AT qiongwu integratedairbornelidardataandimageryforsuburbanlandcoverclassificationusingmachinelearningmethods AT limingdu integratedairbornelidardataandimageryforsuburbanlandcoverclassificationusingmachinelearningmethods AT yuxingeng integratedairbornelidardataandimageryforsuburbanlandcoverclassificationusingmachinelearningmethods AT shisongcao integratedairbornelidardataandimageryforsuburbanlandcoverclassificationusingmachinelearningmethods |
_version_ |
1725062068405534720 |