Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping
Engineering and Technology Journal,
2020, Volume 38, Issue 4A, Pages 510-514
AbstractIn 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
 A.S. Belward, and J.O. Skøien, “Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites,” ISPRS J. Photogramm. Remote Sens., 103, 115–128, 2015..
 D. Lu, and Q. Weng, “A survey of image classification methods and techniques for improving classification performance,” International Journal of Remote Sensing, 28, 5, 823–870, 2007.
 A. Mahmoud, “Plot-based land-cover and soil-moisture mapping using X-L-band SAR data. case study pirna-south, saxony, germany,” Ph.D thesis submitted to Fakultät Forst-, Geo-und Hydrowissenschaften, Technische Universität Dresden, Germany, 2012.
 T. Blaschke, and J. Strobl, “What’s wrong with pixels? Some recent developments interfacing remote sensing and GIS,” GeoBIT/GIS, 6, 1, 12–17, 2001.
 T. Blaschke, C. Burnett, and A. Pekkarinen, “Image segmentation methods for object-based analysis and classification. In: De Meer, F., de Jong, S. Eds, Remote sensing image analysis: including the spatial domain,” Kluwer Academic Publishers, Dordrecht, pp. 211–236, 2004.
 J. S. Walker, and T. Blaschke,. “Object-based land-cover classification for the Phoenix metropolitan area: optimization vs. transportability,” Int. J. Remote Sens., 29, 2021–2040, 2008
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