Vegetation Fuel Type Classification Using Optimised Synergy of Sentinel Data and Texture Feature-Reference-Cited by-同舟云学术

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Vegetation Fuel Type Classification Using Optimised Synergy of Sentinel Data and Texture Feature

Published:2023-07-16 Issue: Volume: Page:
ISSN:
Container-title:IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Mohammadpour Pegah¹,Viegas Domingos Xavier¹,Chuvieco Emilio²,Pereira Alcides³,Mantas Vasco⁴

Affiliation:

1. Univ of Coimbra, ADAI,Department of Mechanical Engineering,Coimbra,Portugal,3030-788

2. Universidad de Alcala, Environmental Remote Sensing Research Group,Department of Geology, Geography and Environment,Alcalá de Henares,Spain,28801

3. Univ Coimbra, Centre for Earth and Space Research of the University of Coimbra,Department of Earth Sciences,Portugal

4. Univ Coimbra, Centre for Earth and Space Research of the University of Coimbra and Marine and Environmental Sciences Centre,Department of Earth Sciences,Portugal

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10281394/10281399/10281659.pdf?arnumber=10281659

Reference10 articles.

1. Generation of fuel type maps from Landsat TM images and ancillary data in Mediterranean ecosystems

2. Improving land cover classification in an urbanized coastal area by random forests: The role of variable selection

3. EVALUATION OF ADVANCED DATA MINING ALGORITHMS IN LAND USE/LAND COVER MAPPING

4. Fuel Loads and Fuel Type Mapping

5. Vegetation Mapping with Random Forest Using Sentinel 2 and GLCM Texture Feature—A Case Study for Lousã Region, Portugal;mohammadpour;Remote Sensing,2022

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