Random forest (RF) and artificial neural network (ANN) algorithms for LULC mapping
Joint Authors
Hamzah, Ammar M.
Shihab, Tay H.
Source
Engineering and Technology Journal
Issue
Vol. 38, Issue 4A (30 Apr. 2020), pp.510-514, 5 p.
Publisher
Publication Date
2020-04-30
Country of Publication
Iraq
No. of Pages
5
Main Subjects
Topics
Abstract EN
In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.
They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods.
Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details.
The classification of both satellite image types is used to extract features and to analyse LULC of the study area.
The results of the classification showed that the artificial neural network method outperforms the random forest method.
The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.
91 and the kappa accuracy was found 0.
89 for the training data set.
While the overall accuracy and the kappa accuracy of the test dataset were found 0.
89 and 0.
87 respectively.
American Psychological Association (APA)
Shihab, Tay H.& al-Hamidawi, Amjad Nasir Muhsin& Hamzah, Ammar M.. 2020. Random forest (RF) and artificial neural network (ANN) algorithms for LULC mapping. Engineering and Technology Journal،Vol. 38, no. 4A, pp.510-514.
https://search.emarefa.net/detail/BIM-972140
Modern Language Association (MLA)
Shihab, Tay H.…[et al.]. Random forest (RF) and artificial neural network (ANN) algorithms for LULC mapping. Engineering and Technology Journal Vol. 38, no. 4A (2020), pp.510-514.
https://search.emarefa.net/detail/BIM-972140
American Medical Association (AMA)
Shihab, Tay H.& al-Hamidawi, Amjad Nasir Muhsin& Hamzah, Ammar M.. Random forest (RF) and artificial neural network (ANN) algorithms for LULC mapping. Engineering and Technology Journal. 2020. Vol. 38, no. 4A, pp.510-514.
https://search.emarefa.net/detail/BIM-972140
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 514
Record ID
BIM-972140