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This content will become publicly available on January 20, 2025

Title: Utilizing the Principal Component Analysis on Multispectral Aerial Imagery for Identification of Underlying Structures
Aerial imagery is a powerful tool when it comes to analyzing temporal changes in ecosystems and extracting valuable information from the observed scene. It allows us to identify and assess various elements such as objects, structures, textures, waterways, and shadows. To extract meaningful information, multispectral cameras capture data across different wavelength bands of the electromagnetic spectrum. In this study, the collected multispectral aerial images were subjected to principal component analysis (PCA) to identify independent and uncorrelated components or features that extend beyond the visible spectrum captured in standard RGB images. The results demonstrate that these principal components contain unique characteristics specific to certain wavebands, enabling effective object identification and image segmentation.  more » « less
Award ID(s):
1920182
NSF-PAR ID:
10543251
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ;
Publisher / Repository:
World Academy of Science, Engineering and Technology
Date Published:
Journal Name:
International Journal of Computer and Information Engineering
Volume:
18
Issue:
8
ISSN:
ISNI:0000000091950263
Page Range / eLocation ID:
543-547
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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