MACHINE LEARNING ALGORITHMS: OBJECT IDENTIFICATION BASED ON FEATURE EXTRACTION FROM MULTISPECTRAL SATELLITE IMAGES USING PRINCIPAL COMPONENT ANALYSIS - G. Sreenivasulu,Mundluru Dharani
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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 ... Full description
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Description
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.
More Information
| Author | G. Sreenivasulu, Mundluru Dharani |
|---|---|
| Publisher | LAP LAMBERT Academic Publishing |
| Release year | 2023 |
| Cover type | Softcover |
| EAN | 9786206142188 |