Towards improved land use mapping of irrigated croplands: performance assessment of different image classification algorithms and approaches. Issue 1 (1st January 2017)

• Record Type: Journal Article
• Authors: Basukala, Amit Kumar; Oldenburg, Carsten; Schellberg, Jürgen; Sultanov, Murodjon; Dubovyk, Olena
• Is Part Of: European journal of remote sensing. Volume 50:Issue 1(2017)
• Page Start: 187
• Page End: 201
• Publication Date: 2017-01-01
• Subjects: Land use (LU) mapping -- random forest -- support vector machine -- maximum likelihood -- field-based -- Uzbekistan; Remote sensing -- Periodicals; Remote sensing; Electronic journals; Periodicals; 621.3678
• DOI: 10.1080/22797254.2017.1308235
• Languages: English
• ISSNs: 2279-7254

Abstract

ABSTRACT: Accurate agricultural land use (LU) map is essential for many agro-environmental applications. With advances in technology, object-based image classification and non-parametric machine learning algorithms evolved. Still, no particular method has universal applicability. This paper compares robust non-parametric machine learning algorithms, random forest (RF) and support vector machine (SVM), and a common parametric algorithm maximum likelihood (MLC) based on multiple Landsat 8 images. We have also assessed the classifier performance relative to the choice either pixel-based (PB) or field-based (FB) approach. The study area, a semi-desert irrigated region, lies in Khorezm province and Republic of Karakalpakstan in Uzbekistan. Accuracy assessment showed higher overall accuracy (OA) and kappa index (KI) of the nonparametric machine learning FB-RF and FB-SVM algorithms over the PB-RF, PB-SVM and PB-MLC algorithms. The lowest OA and KI occurred with the parametric FB-MLC. Based on the results, the FB machine learning non-parametric algorithms are recommended for mapping irrigated croplands.