International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences | |
A FIT-FOR-PURPOSE ALGORITHM FOR ENVIRONMENTAL MONITORING BASED ON MAXIMUM LIKELIHOOD, SUPPORT VECTOR MACHINE AND RANDOM FOREST | |
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
【 预 览 】
Files | Size | Format | View |
---|---|---|---|
RO201911046405910ZK.pdf | 1569KB | download |