Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification - Info and Reading Options
By Anil Kumar, A. Senthil Kumar and Priyadarshi Upadhyay
"Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification" was published by Taylor & Francis Group in 2020, it has 194 pages and the language of the book is English.
“Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification” Metadata:
- Title: ➤ Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification
- Authors: Anil KumarA. Senthil KumarPriyadarshi Upadhyay
- Language: English
- Number of Pages: 194
- Publisher: Taylor & Francis Group
- Publish Date: 2020
“Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification” Subjects and Themes:
- Subjects: ➤ Geography - Remote-sensing images - Classification - Data processing - Machine learning - Fuzzy algorithms - Images-satellite - Informatique - Apprentissage automatique - Algorithmes flous - COMPUTERS - Machine Theory - TECHNOLOGY - Environmental Engineering & Technology - Imaging Systems
Edition Identifiers:
- The Open Library ID: OL29504178M - OL21688051W
- Online Computer Library Center (OCLC) ID: 1173541732 - 1142359265
- Library of Congress Control Number (LCCN): 2020019411 - 2020019410
- ISBN-13: 9780429340369
- All ISBNs: 9780429340369
AI-generated Review of “Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification”:
"Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification" Description:
The Open Library:
"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 is discussed. Accuracy assessment for soft classification outputs are included and all algorithms are supported by in-house developed tool as Sub-pixel Multi-spectral Image Classifier"--
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