Prediction of Urban Area Expansion with Implementation of MLC, SAM and SVMs’ Classifiers Incorporating Artificial Neural Network Using Landsat Data

Prediction of Urban Area Expansion with Implementation of MLC, SAM and SVMs’ Classifiers Incorporating Artificial Neural Network Using Landsat Data

A reliable land cover (LC) map is essential for planners, as missing proper land cover maps may deviate a project. This study is focusing on land cover classification and prediction using three well known classifiers and remote sensing data. Maximum Likelihood classifier (MLC), Spectral Angle Mapper...

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Bibliographic Details
Main Authors: Saeid Zare Naghadehi, Milad Asadi, Mohammad Maleki, Seyed-Mohammad Tavakkoli-Sabour, John Lodewijk Van Genderen, Samira-Sadat Saleh
Format: Article
Language:English
Published: MDPI AG 2021-07-01
Series:ISPRS International Journal of Geo-Information
Subjects:
urban expansion
change detection
change prediction
Support Vector Machines (SVMs)
Maximum Likelihood Classifier (MLC)
Spectral Angle Mapper (SAM)
Online Access:https://www.mdpi.com/2220-9964/10/8/513

Internet

https://www.mdpi.com/2220-9964/10/8/513

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