Evaluation of Machine Learning Algorithms in the Classification of Multispectral Images from the Sentinel-2A/2B Orbital Sensor for Mapping the Environmental Dynamics of Ria Formosa (Algarve, Portugal)

被引:1
|
作者
da Souza, Flavo Elano Soares [1 ]
Rodrigues, Jose Inacio de Jesus [2 ]
机构
[1] Univ Fed Rio Grande do Norte, Agr Sch Jundiai, POB 7, BR-59280000 Macao, Brazil
[2] Univ Algarve, Dept Civil Engn, P-8005139 Faro, Portugal
关键词
remote sensing; GIS; machine learning; image classification; barrier islands; environmental monitoring;
D O I
10.3390/ijgi12090361
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the growing availability of remote sensing orbital spatial data, the applications of machine learning (ML) algorithms have been leveraging the field of process automation in image classification. The present work aimed to evaluate the precision and accuracy of ML algorithms in the classification of Sentinel 2A/2B images from an area of high environmental dynamics, such as Ria Formosa (Algarve, Portugal). The images were submitted to classification by groups of ML algorithms such as the Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Decision Tree (DT). The Orfeo Toolbox (OTB) open-source programming package made the algorithms available. Ten samples were collected for each of the 14 land use and cover classes in the Ria Formosa area, totaling 140 samples. Of these, 70% were for training and 30% for validating the classification. The evaluation metrics used were the class discrimination measures: Recall (R), the Global Kappa Index (k), and the General Accuracy Index (OA). The results showed that the KNN and DT algorithms demonstrated a greater discrimination capacity for most classes. SVM and RF significantly improved class discrimination when using larger samples for training. Merging the classified images significantly improved the classification accuracy, ranging from 71% to 81%. This evaluation made it possible to define sets of ML algorithms sensitive to change detection for mapping and monitoring dynamic environments.
引用
收藏
页数:24
相关论文
共 4 条
  • [1] Snow Coverage Mapping by Learning from Sentinel-2 Satellite Multispectral Images via Machine Learning Algorithms
    Wang, Yucheng
    Su, Jinya
    Zhai, Xiaojun
    Meng, Fanlin
    Liu, Cunjia
    REMOTE SENSING, 2022, 14 (03)
  • [2] Evaluation of machine learning algorithms for forest stand species mapping using Sentinel-2 imagery and environmental data in the Polish Carpathians
    Grabska, Ewa
    Frantz, David
    Ostapowicz, Katarzyna
    REMOTE SENSING OF ENVIRONMENT, 2020, 251
  • [3] Google's Cloud Computing Platform-Based Performance Assessment of Machine Learning Algorithms for Precisely Maize Crop Mapping Using Integrated Satellite Data of Sentinel-2A/B and Planetscope
    Kumar, Himanshu
    Kumar, Rohan
    Dutta, Sujay
    Singh, Magan
    JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING, 2023, 51 (12) : 2599 - 2613
  • [4] Google’s Cloud Computing Platform-Based Performance Assessment of Machine Learning Algorithms for Precisely Maize Crop Mapping Using Integrated Satellite Data of Sentinel-2A/B and Planetscope
    Himanshu Kumar
    Rohan Kumar
    Sujay Dutta
    Magan Singh
    Journal of the Indian Society of Remote Sensing, 2023, 51 : 2599 - 2613