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The book 'Remote Sensing Data Analysis in R' serves as a comprehensive guide for professionals seeking to harness the power of R for remote sensing and GIS operations. Authored by experts Alka Rani, Nirmal Kumar, S.K. Singh, N.K. Sinha, R.K. Jena, and Himesh Patra, this hardback edition caters particularly to individuals who possess a fundamental understanding of remote sensing and GIS, yet require guidance in utilizing R software. With detailed, step-by-step instructions, readers can learn how to download, install, and effectively use R for various remote sensing applications. The book covers essential tasks ranging from loading and plotting both raster and vector data to advanced methodologies, including unsupervised and supervised classification, thematic mapping, and the use of complex machine learning algorithms such as random forests and support vector machines. Throughout the text, users are equipped with practical R-codes and the necessary background to conduct thorough data analysis, ensuring they are well-prepared to tackle real-world remote sensing challenges.
Key Features
| Features | Description |
|---|---|
| Comprehensive Guide | Detailed instructions and R-codes for remote sensing and GIS operations |
| User-Friendly | Designed for users with basic knowledge of remote sensing and GIS |
| Step-by-Step Instructions | Clear guidance for downloading and installing R software |
| Data Processing Techniques | In-depth coverage of raster and vector data processing |
| Advanced Machine Learning Integration | Includes R-codes for random forest and support vector machine algorithms |
| Thematic Mapping | Guidance on creating thematic maps for effective data visualization |
| Attributes | Description |
|---|---|
| Format | Hardback |
| Authors | Alka Rani, Nirmal Kumar, S.K. Singh, N.K. Sinha, R.K. Jena, Himesh Patra |
| Target Audience | Users with basic understanding of remote sensing and GIS |
| Software Compatibility | R (free and open-source software) |
| Key Topics | Data Availability, Radiometric Calibration, Coordinate Reference Systems (CRS), Raster and Vector Analysis, Classification, Thematic Mapping |
| Publication Date | 2023 |
*Disclaimer: The above description has been AI-generated and has not been audited or verified for accuracy. It is recommended to verify product details independently before making any purchasing decisions.
This book is tailored for users who have a basic understanding of remote sensing and GIS but may have limited experience with R software.
The book provides guidance for loading, processing, and analyzing both raster and vector data.
Yes, it includes R-codes for implementing advanced machine learning algorithms such as random forest and support vector machines for supervised classification.
Absolutely! The book contains R-codes along with practical examples that facilitate hands-on learning.
Yes, R is a free and open-source software that can be easily downloaded and installed.
Brand: nipa
Country Of Origin: India
The book introduces the R software and provides step-by-step instructions for downloading and installing it. It offers R-codes for tasks such as loading and plotting both raster and vector data, pre-processing and filtering raster data, processing vector data, unsupervised and supervised classification of raster data, and thematic mapping of both raster and vector data. Furthermore, it provides R-codes for advanced machine learning algorithms like random forest and support vector machine for supervised classification of raster data.
Content of the book Remote Sensing Data Analysis in R
Download and Installation of R
Data availability and downloading
Raster data in R
Radiometric Calibration
Vector data in R
Coordinate Reference Systems (CRS) in R
Subset Raster
Vector Data Analysis
Mosaic Raster Images
Resampling of Raster Images
Raster data statistics
Image Contrast Enhancement
Spatial Filters
Transformations
Unsupervised Classification
Supervised Classification
Digital terrain analysis
Thematic mapping
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