Paper Info
Title
The use of satellite SAR imagery to crop classification in Ukraine within JECAM project
Abstract
In this paper, we focus on the application of satellite synthetic-aperture radar (SAR) images for discriminating summer crops in Ukraine within the JECAM project. Both optical (EO-1/ALI) and SAR (RADARSAT-2) images are used in order to assess impact adding SAR images for classification purposes. Three different classifiers, in particular neural networks, support vector machine and decision trees, are applied with neural networks giving the best overall accuracy. It is found that major impact of using SAR images is for sunflower and sugar beet classes while there was no gain for other crops (maize and soybeans).
Year
DOI
Venue
2014
10.1109/IGARSS.2014.6946721
Geoscience and Remote Sensing Symposium
Keywords
Field
DocType
geophysical image processing,image classification,neural nets,radar imaging,remote sensing by radar,support vector machines,synthetic aperture radar,vegetation mapping,ALI images,EO-1 images,JECAM project,RADARSAT-2 image,SAR images,Ukraine,crop classification,decision trees,maize,optical images,particular neural networks,satellite synthetic-aperture radar images,soybeans,sugar beet classes,sunflower,support vector machine,JECAM,SAR,Ukraine,classification,crop
Radar,Decision tree,Computer vision,Satellite,Computer science,Crop,Remote sensing,Support vector machine,Artificial intelligence,Artificial neural network
Conference
ISSN
Citations 
PageRank 
2153-6996
6
0.69
References 
Authors
8
4
Name
Order
Citations
PageRank
Nataliia Kussul119125.01
Sergii Skakun213017.69
Andrii Shelestov314519.39
Olga Kussul4527.42