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MACHINE LEARNING ALGORITHMS
Details
Remote sensing is immensely useful for gathering data from Earth-surface objects without coming into direct contact with them. Multi-spectral satellite images are essential for data analysis in the fields of agriculture, regional planning, geology, meteorology, forestry, landscape, biodiversity conservation. Enhancement is the best method for extracting more information from a large dataset. The information increases the number of pixels that can effectively represent the image's information and display its spectral features. These values are maintaining the trade-off balance between spatial and spectral values with dimensional reduction technique and Machine Learning algorithms.
Autorentext
I, (Dr. M. DHARANI), heartfully express my gratitude to my research supervisor Dr. G. Sreenivasulu, Professor, Department of ECE, S.V.University College of Engineering. His advice, Scholarly guidance, Pragmatic approach and erudite discussions have enabled me to successfully complete this research.
Weitere Informationen
- Allgemeine Informationen
- Sprache Englisch
- Anzahl Seiten 156
- Herausgeber LAP LAMBERT Academic Publishing
- Gewicht 250g
- Untertitel OBJECT IDENTIFICATION BASED ON FEATURE EXTRACTION FROM MULTISPECTRAL SATELLITE IMAGES USING PRINCIPAL COMPONENT ANALYSIS
- Autor Mundluru Dharani , G. Sreenivasulu
- Titel MACHINE LEARNING ALGORITHMS
- Veröffentlichung 24.11.2023
- ISBN 6206142183
- Format Kartonierter Einband
- EAN 9786206142188
- Jahr 2023
- Größe H220mm x B150mm x T10mm
- GTIN 09786206142188