This book brings consolidated information in the form of fuzzy machine and deep learning models for single class mapping from multi-sensor multi-temporal remote sensing images at one place. It provides information about capabilities of multi-spectral and hyperspectral images, importance of dimensionality reduction, various spectral and texture-based indices, single, dual, or multi-sensor temporal sensor concepts, fuzzy machine learning models capable for single class mapping and associated deep learning-based models supported by case studies.
Provides detailed exposition to (hyperspectral and multispectral) remote sensing and related image processing, fuzzy set theoretic image processing and deep learning methods
Focusses on use of single, dual, multi-sensor multi-temporal data application for specific single class mapping
Reviews pre-processing of multi-sensor multi-temporal remote sensing data set and hyperspectral data set
Discusses both traditional machine learning and deep learning approaches
Includes case studies from crop mapping, forest species mapping, and stubble burnt paddy fields
This book is aimed at researchers and graduate students in remote sensing, image processing, environmental engineering, geomatics, and geoinformatics.
- ISBN:
- 9781032428321
- 9781032428321
-
Category:
- Geographical information systems (GIS) & remote sensing
- Format:
- Hardback
- Publication Date:
-
09-06-2023
- Publisher:
- Taylor & Francis Ltd
- Country of origin:
- United Kingdom
- Pages:
- 148
- Dimensions (mm):
- 234x156mm
- Weight:
- 0.45kg
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