Extended morphological profiles analysis of airborne hyperspectral image classification using machine learning algorithms
| Autor: | S. Rama Subramoniam, R. Anand, S. Veni, P. Geetha |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Support vector machine
Computer science business.industry Wavelength Hyperspectral imaging Pattern recognition QA75.5-76.95 Classification Object (computer science) Class (biology) Empirical morphological profiles Hyperspectral image Electronic computers. Computer science Face (geometry) Machine learning Hyperspectral image classification Artificial intelligence business Closing (morphology) Curse of dimensionality |
| Zdroj: | International Journal of Intelligent Networks, Vol 2, Iss, Pp 1-6 (2021) |
| ISSN: | 2666-6030 |
| Popis: | When morphological capabilities are used for the class of high decision hyperspectral photographs from metropolitan areas, one must not forget two crucial problems. Among which the primary one is that traditional morphological openings and closings degrade the object obstacles and distorts the items shape. Morphological profiles (MP) opening and closing via reconstruction can keep us away from this problem, however this system ends in a few unwanted consequences. In this paper, first check out morphological summaries with subjective restoration and steering MPs for the classification of excessive decision hyperspectral snap shots from city areas. Secondly, broaden a supervised face extraction to lessen the dimensionality of the engendered morphological profiles for the prediction. |
| Databáze: | OpenAIRE |
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