MACHINE LEARNING ALGORITHMS: OBJECT IDENTIFICATION BASED ON FEATURE EXTRACTION FROM MULTISPECTRAL SATELLITE IMAGES USING PRINCIPAL COMPONENT ANALYSIS - G. Sreenivasulu,Mundluru Dharani
-30% ar kodu BOOKS
Piegāde 15-21 darba dienu laikā
30 dienu atgriešanas politika
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 ... Pilns apraksts
Jums varētu patikt arī
Aprašymas
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.
Vairāk informācijas
| Autors | G. Sreenivasulu, Mundluru Dharani |
|---|---|
| Izdevējs | LAP LAMBERT Academic Publishing |
| Izlaides gads | 2023 |
| Vāka tips | Mīkstais vāks |
| EAN | 9786206142188 |