Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników

Znaleziono wyników: 1

Liczba wyników na stronie
first rewind previous Strona / 1 next fast forward last
Wyniki wyszukiwania
help Sortuj według:

help Ogranicz wyniki do:
first rewind previous Strona / 1 next fast forward last
EN
The remote sensing technique is crucial for creating maps showing land use and land cover from a procedure known as image classification. For the process of image classification to be successful, many aspects must be taken into consideration; one of these factors is the availability of high-quality Landsat images. This study aims to classify and map the studied area’s land use and cover using remote sensing and geographic information system techniques. This study is divided into two parts: part one focuses on classifying land use and land cover, while part two evaluates how accurate the classification is. Several classification methods are compared for their efficacy in this study. Some image classification methods have shown promising results when used to remote sensing data. An efficient classifier is necessary for extracting data from remote-sensing images. The maximum likelihood classification was the most effective classifier in our study. In this study, the Maximum Likelihood classification accuracy has achieved an overall accuracy of 91% and an overall kappa accuracy of 86.83%. This study provides essential data for planners and decision-makers to design sustainable environments.
first rewind previous Strona / 1 next fast forward last
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.