Ground surface structure classification using UAV remote sensing images and machine learning algorithms

被引:1
|
作者
Fan, Ching Lung [1 ]
机构
[1] Republ China Mil Acad, Dept Civil Engn, Kaohsiung, Taiwan
关键词
Land use; Land cover; Machine learning; RGB images; Classification; LAND-COVER CLASSIFICATION; RANDOM FOREST; SPATIAL-RESOLUTION; URBAN VEGETATION; EXTRACTION; BUILDINGS; ACCURACY; DAMAGE;
D O I
10.1007/s12518-023-00530-x
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
The applicability of a machine learning algorithm can vary across regions due to disparities in image data sources, preprocessing techniques, and model training. To enhance the classification accuracy of ground surface structures, it is crucial to select an appropriate method tailored to the specific region. This study used highly-efficient UAV remote sensing photography and conducted training and tests using three supervised machine learning techniques, namely support vector machine (SVM), random forest (RF), and maximum likelihood (ML) as well as performed a cluster analysis using an unsupervised machine learning technique. The main objective of this study was to evaluate the effectiveness of four machine learning methods for classifying five distinct structures (forest, grassland, bare land, built-up area, and road) in UAV images. The machine learning methods will be trained using sample features extracted from the UAV images, and test classifications will be conducted for the five ground surface structures. The results demonstrated that the RF classifier outperformed the other methods, achieving performance metrics, including an accuracy of 91.78%, an area under the curve (AUC) of 0.93, a Kappa coefficient of 0.88, and a gain of 100%. The RF classifier showcased its capability to accurately differentiate between various ground surface structures by examining spectral composition, encompassing both natural and artificial elements, and making precise judgments based on factors such as color, color tone, and texture observed in the images.
引用
收藏
页码:919 / 931
页数:13
相关论文
共 50 条
  • [1] Ground surface structure classification using UAV remote sensing images and machine learning algorithms
    Ching Lung Fan
    [J]. Applied Geomatics, 2023, 15 : 919 - 931
  • [2] Stochastic learning algorithms for the classification of remote-sensing images
    Diotalevi, F.
    Valle, M.
    [J]. Alta Frequenza Rivista Di Elettronica, 2001, 13 (05): : 60 - 64
  • [3] Above-Ground Biomass Estimation in Oats Using UAV Remote Sensing and Machine Learning
    Sharma, Prakriti
    Leigh, Larry
    Chang, Jiyul
    Maimaitijiang, Maitiniyazi
    Caffe, Melanie
    [J]. SENSORS, 2022, 22 (02)
  • [4] Classification of Australian Native Forest Species Using Hyperspectral Remote Sensing and Machine-Learning Classification Algorithms
    Shang, Xiao
    Chisholm, Laurie A.
    [J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2014, 7 (06) : 2481 - 2489
  • [5] Mangrove LAI estimation based on remote sensing images and machine learning algorithms
    Fu, Bolin
    Sun, Jun
    Li, Yuyang
    Zuo, Pingping
    Deng, Tengfang
    He, Hongchang
    Fan, Donglin
    Gao, Ertao
    [J]. Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, 2022, 38 (07): : 218 - 228
  • [6] Ensemble of Machine Learning Algorithms for Rice Grain Yield Prediction Using UAV-Based Remote Sensing
    Sarkar, Tapash Kumar
    Roy, Dilip Kumar
    Kang, Ye Seong
    Jun, Sae Rom
    Park, Jun Woo
    Ryu, Chan Seok
    [J]. JOURNAL OF BIOSYSTEMS ENGINEERING, 2024, 49 (01) : 1 - 19
  • [7] Assessing surface water pollution in Hanoi, Vietnam, using remote sensing and machine learning algorithms
    Do, Thi-Nhung
    Nguyen, Diem-My Thi
    Ghimire, Jiwnath
    Vu, Kim-Chi
    Do Dang, Lam-Phuong
    Pham, Sy-Liem
    Pham, Van-Manh
    [J]. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2023, 30 (34) : 82230 - 82247
  • [8] Classification of RASAT Satellite Images Using Machine Learning Algorithms
    Abujayyab, Sohaib K. M.
    Yucer, Emre
    Karas, I. R.
    Gultekin, I. H.
    Abali, O.
    Bektas, A. G.
    [J]. 6TH INTERNATIONAL CONFERENCE ON SMART CITY APPLICATIONS, 2022, 393 : 871 - 882
  • [9] Assessing surface water pollution in Hanoi, Vietnam, using remote sensing and machine learning algorithms
    Thi-Nhung Do
    Diem-My Thi Nguyen
    Jiwnath Ghimire
    Kim-Chi Vu
    Lam-Phuong Do Dang
    Sy-Liem Pham
    Van-Manh Pham
    [J]. Environmental Science and Pollution Research, 2023, 30 : 82230 - 82247
  • [10] Adoption of Machine Learning in Intelligent Terrain Classification of Hyperspectral Remote Sensing Images
    Li, Yanyi
    Wang, Jian
    Gao, Tong
    Sun, Qiwen
    Zhang, Liguo
    Tang, Mingxiu
    [J]. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2020, 2020