Increasing The Taxonomic Accuracy of Remote Sensing Data Using Traditional Pattern Recognition Methods

Alhan Anwer Younis Alsafar ()

Technium, 2022, vol. 4, issue 1, 1-10

Abstract: The most significant outcomes of remote sensing are maps of Land Use and Land Cover (LULC), which can be controlled by a procedure known as image classification. Using image processing methods, we are developing a system in this effort to categorize satellite photos and extract information. Satellite pictures have been classified into usable and unused areas, as well as sub-classifying each class into numerous further classes. Used satellite photos are further divided into residential, commercial, highway, and agricultural areas, while unused images are divided into forest, river, desert, and beach areas. Since K-means classifiers analyze images features and Otsu's approach for multilevel image thresholding is efficient for automatic classification of satellite images, that is the main topic of this research

Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://techniumscience.com/index.php/technium/article/view/7568/2743 (application/pdf)
https://techniumscience.com/index.php/technium/article/view/7568 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:tec:techni:v:4:y:2022:i:1:p:1-10

DOI: 10.47577/technium.v4i8.7252

Access Statistics for this article

Technium is currently edited by Scurtu Ionut Cristian

More articles in Technium from Technium Science
Bibliographic data for series maintained by Ana Maria Golita ().