A novel ensemble learning approach to extract urban impervious surface based on machine learning algorithms using SAR and optical data-Reference-Cited by-同舟云学术

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A novel ensemble learning approach to extract urban impervious surface based on machine learning algorithms using SAR and optical data

Published:2024-08 Issue: Volume:132 Page:104013
ISSN:1569-8432
Container-title:International Journal of Applied Earth Observation and Geoinformation
language:en
Short-container-title:International Journal of Applied Earth Observation and Geoinformation

Author:

Ahmad Muhammad Nasar,Shao Zhenfeng,Xiao Xiongwu,Fu Peng,Javed Akib,Ara Iffat

Publisher

Elsevier BV

Reference40 articles.

1. Ecosystem structure along bioclimatic gradients in Hawai’i from imaging spectroscopy;Asner;Remote Sens. Environ.,2005

2. Automatic Extraction of Urban Impervious Surface Based on SAH-Unet;Chang;Remote Sens.,2023

3. A nighttime lights adjusted impervious surface index (NAISI) with integration of Landsat imagery and nighttime lights data from International Space Station;Chen;Int. J. Appl. Earth Obs. Geoinf.,2019

4. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation;Chicco;BMC Genomics,2020

5. Novel method of building train and test sets for evaluation of machine learning models related to software bugs assignment;Chmielowski;Sci. Rep.,2023

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