Remote Sensing | |
Classifying Complex Mountainous Forests with L-Band SAR and Landsat Data Integration: A Comparison among Different Machine Learning Methods in the Hyrcanian Forest | |
Sara Attarchi1  | |
[1]Remote Sensing Group, Institute of Geology, TU Bergakademie Freiberg (TUBAF), Bernhard-von-Cotta-Str. 2, D-09599 Freiberg, |
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关键词: Landsat; ALOS/PALSAR; L-band; maximum likelihood classification; support vector machines; neural networks; random forest; topographic effects; Hyrcanian mountainous forest; Iran; | |
DOI : 10.3390/rs6053624 | |
来源: mdpi | |
【 摘 要 】
Forest environment classification in mountain regions based on single-sensor remote sensing approaches is hindered by forest complexity and topographic effects. Temperate broadleaf forests in western Asia such as the Hyrcanian forest in northern Iran have already suffered from intense anthropogenic activities. In those regions, forests mainly extend in rough terrain and comprise different stand structures, which are difficult to discriminate. This paper explores the joint analysis of Landsat7/ETM+, L-band SAR and their derived parameters and the effect of terrain corrections to overcome the challenges of discriminating forest stand age classes in mountain regions. We also verified the performances of three machine learning methods which have recently shown promising results using multisource data; support vector machines (SVM), neural networks (NN), random forest (RF) and one traditional classifier (
【 授权许可】
CC BY
© 2014 by the authors; licensee MDPI, Basel, Switzerland
【 预 览 】
Files | Size | Format | View |
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RO202003190026783ZK.pdf | 1756KB | download |