A Fit-for-Purpose algorithm for Environmental Monitoring based on Maximum likelihood, Support Vector Machine and Random Forest

被引：8

作者：

Jamali, Ali ^{[1
]}

机构：

[1] Apadana Inst Higher Educ, Fac Surveying Engn, Shiraz, Iran

来源：

ISPRS TECHNICAL COMMISSION III WG III/2, 10 JOINT WORKSHOP MULTIDISCIPLINARY REMOTE SENSING FOR ENVIRONMENTAL MONITORING | 2019年 / 42-3卷 / W7期

关键词：

Image classification; Earth Observation; Support Vector Machine; Random Forest; R; LAND-COVER CLASSIFICATION; LEARNING ALGORITHMS; CLIMATE; MODEL;

D O I：

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

中图分类号：

P9 [自然地理学];

学科分类号：

0705 ; 070501 ;

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.

引用

页码：25 / 32

页数：8

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