Publication Type : Journal Article
Source : International Journal of Intelligent Networks
Url : https://www.sciencedirect.com/science/article/pii/S2666603021000014
Campus : Coimbatore
School : School of Engineering
Department : Electrical and Electronics
Year : 2021
Abstract : 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.
Cite this Research Publication : Anand, R., Veni, S., Geetha, P., & Subramoniam, S. R. (2021). Extended morphological profiles analysis of airborne hyperspectral image classification using machine learning algorithms. International Journal of Intelligent Networks, 2, 1-6.