Paper Info
Title
Land use and cover classification of Sentinel-IA SAR imagery: A case study of Istanbul.
Abstract
In this study, Sentinel-IA SAR imagery for land use/cover classification and its impacts on classification algorithms were addressed. Sentinel-1A imagery has dual polarization (VV and VH) and freely available from ESA. Istanbul was selected as the study region. After the pre-processing steps including the applying the precise orbit file, calibration, multilooking, speckle filtering and terrain correction, the imagery was classified as the following step. Three classification algorithms (SVM, RF and K-NN) were implemented and the impacts of additional bands (VV-VH, VV+VH etc.) were investigated. Results demonstrated that highest classification accuracy of this study was obtained by SVM classification with the original bands (VV and VH) of Sentinel-IA imagery. Moreover, it was concluded that additional bands had different impacts on each classifier within accuracy.
Year
Venue
Keywords
2017
Signal Processing and Communications Applications Conference
Sentinel 1-A,Synthetic aperture radar,support vector machines,random forest,classification
Field
DocType
ISSN
Computer vision,Radar imaging,Pattern recognition,Speckle pattern,Synthetic aperture radar,Computer science,Support vector machine,Terrain,Filter (signal processing),Artificial intelligence,Statistical classification,Classifier (linguistics)
Conference
2165-0608
Citations 
PageRank 
References 
0
0.34
4
Authors
4
Name
Order
Citations
PageRank
Ustuner, Mustafa122.07
Fusun Balik Sanli284.51
Gökhan Bilgin36213.18
Saygin Abdikan4123.70