A FIT-FOR-PURPOSE ALGORITHM FOR ENVIRONMENTAL MONITORING BASED ON MAXIMUM LIKELIHOOD, SUPPORT VECTOR MACHINE AND RANDOM FOREST

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Jamali, A.^1¹ 

[1] Faculty of Surveying Engineering, Apadana Institute of Higher Education, Shiraz, Iran^1

关键词: Image classification;    Earth Observation;    Support Vector Machine;    Random Forest;    R;   

DOI  :  10.5194/isprs-archives-XLII-3-W7-25-2019

学科分类：地球科学(综合)

来源: Copernicus Publications

【 摘 要 】

Due to concerns of recent earth climate changes such as an increase of earth surface temperature and monitoring its effect on earth surface, environmental monitoring is a necessity. Environmental change monitoring in earth sciences needs land use land cover change (LULCC) modelling as a key factor to investigate impact of climate change phenomena such as droughts and floods on earth surface land cover. There are several free and commercial multi/hyper spectral data sources of Earth Observation (EO) satellites including Landsat, Sentinel and Spot. In this paper, for land use land cover modelling (LULCM), image classification of Landsat 8 using several mathematical and machine learning algorithms including Support Vector Machine (SVM), Random Forest (RF), Maximum Likelihood (ML) and a combination of SVM, ML and RF as a fit-for-purpose algorithm are implemented in R programming language and compared in terms of overall accuracy for image classification.

【 授权许可】

CC BY