Details

Autor(en) / Beteiligte

• Kumar, Anil
• Upadhyay, Priyadarshi
• Kumar, A. Senthil

Titel

Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification

Auflage

1st edition

Ort / Verlag

Milton: CRC Press

Erscheinungsjahr

2021

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• This book covers the state of art image classification methods for discrimination of earth objects from remote sensing satellite data with an emphasis on fuzzy based learning methods including applications for preparing land cover classification outputs from actual satellite data. It provides details about the temporal indices database using proposed class-based sensor independent approach supported by practical examples. Fuzzy based algorithms with machine learning algorithms to prepare land cover maps are discussed. Accuracy assessment for soft classification outputs is included and all algorithms are supported by in-house developed tools such as the sub-pixel multi-spectral image classifier. Aimed at researchers; graduate students; and professionals in earth remote sensing, remote sensing image and data processing, geography and geoinformation science, and image classification, this book: Exclusively focuses on using fuzzy classification to remote sensing images Covers sub-pixel multi-spectral image classifier tools (SMIC) to support discussed algorithms Explains fuzzy and learning based classifiers with in-house developed SMIC tools Discusses ANN, CNN, RNN, and hybrid learning classifiers application on remote sensing images Combines explanation of the algorithms with examples, graphs, and charts.