Hierarchical classification of Sentinel 2-a images for land use and land cover mapping and its use for the CORINE system

Date

2020-06-01

Author

Demirkan, Doga C.
Koz, Alper
Duzguna, H. Sebnem

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

427
views

0
downloads

The aim of this study is to investigate the potential of the Sentinel-2 satellite for land use and land cover (LULC) mapping. The commonly known supervised classification algorithms, support vector machines (SVMs), random forest (RF), and maximum likelihood (ML) classification are adopted for investigation along with a proposed hierarchical classification model based on a coordination of information on the environment land cover system. The main classes for land cover and mapping in the proposed hierarchical classification are selected as water, vegetation, built-up, and bare land in the first level, which is followed by inland water, marine water, forest/meadow, vegetated agricultural land, barren land, and nonvegetated agricultural land in the second level. The study areas for the experiments are selected as the two biggest cities of Turkey, namely Ankara and Izmir, providing a sufficient number of classes for comparison purposes. During the utilized hierarchical methodology, water and vegetation are first extracted using the normalized difference water and vegetation indices. This is followed by the selection of training pixels from the remaining classes to perform and compare different supervised learning algorithms for the first- and second-level classification in terms of accuracy rates. The experimental results first reveal that while the SVMs have close accuracy performances to those with RF, they are significantly superior to the ML classification, with an average of 8% accuracy rates for LULC mapping. Second, the hierarchical classification also gives higher performances with respect to the nonhierarchical classification, with the provided gains between 4% and 10% for class-based accuracies. The overall accuracy rates of the proposed hierarchical methodology are 85% and 84% for the first-level classes and 83% and 72% for the second-level classes, respectively, for Izmir and Ankara. (C) 2020 Society of Photo-Optical Instrumentation Engineers (SPIE)

Subject Keywords

General Earth and Planetary Sciences

URI

https://hdl.handle.net/11511/62753

Journal

JOURNAL OF APPLIED REMOTE SENSING

DOI

https://doi.org/10.1117/1.jrs.14.026524

Collections

Center for Image Analysis (OGAM), Article

Suggestions

OpenMETU
Core

Geometric correction accuracy of IRS-1D PAN imagery using topographic map versus GPS control points Turker, M; Gacemer, AO (Informa UK Limited, 2004-03-01) Geometric correction accuracy of IRS-1D panchromatic imagery was investigated using GPS- and 1 : 25 000 scale topographic map-derived control points. The differentially corrected GPS-derived coordinates provided markedly more accurate results than did uncorrected handheld GPS- and map-derived GCPs. The rms error value of differentially corrected GPS-derived control points based on second-degree polynomial was in the order of +/-3 m. Geometric corrections made with second-degree polynomials, using both the m...
Simulation of event-based snowmelt runoff hydrographs based on snow depletion curves and the degree-day method Sensoy, A; Tekeli, AE; Forman, AA; Sorman, AU (Informa UK Limited, 2003-12-01) In mountainous regions, runoff from snowmelt is an important hydrologic and economic factor, and therefore early runoff forecasting brings significant advantages to the planning and operation of water resources systems. Similarly, predicting runoff from snowmelt during spring months in the eastern part of Turkey, where most of the water originates from high mountains and contributes to the large dams on the Euphrates River, is of crucial importance. In this study, an event-type, physically based hydrologic ...
Evaluation of cross-track illumination in EO-1 Hyperion imagery for lithological mapping San, B. Taner; Süzen, Mehmet Lütfi (Informa UK Limited, 2011-01-01) Hyperspectral remote sensing data is a powerful tool for discriminating lithological units and for the preparation of mineral maps for alteration studies. The spaceborne hyperspectral Hyperion sensor, despite its narrow swath width (similar to 7.5 km), possesses great potential with its 196 channels within the wavelength range 426.82-2395.50 nm. Although it has many advantages such as low cost and on-demand coverage, much uncertainty exists in the utility of its applications. For example, poor signal-to-noi...
Integration of environmental variables with satellite images in regional scale vegetation classification Domaç, Ayşegül; Süzen, Mehmet Lütfi; Bilgin, Cemal Can (Informa UK Limited, 2006-04-01) The difficulty of collecting information at conventional field studies and relatively coarse spatial and spectral resolution of Landsat images forced the use of environmental variables as ancillary data in vegetation mapping. The aim of this study is to increase the accuracy of species level vegetation classification incorporating environmental variables in the Amanos Mountains region of southern central Turkey. In the first part of the study, ordinary vegetation classification is attained by using a maximu...
Hierarchical land use and land cover classification of Sentinel 2-a images and its use for corine system Demirkan, Doğa Çağdaş; Akyürek, Sevda Zuhal; Koz, Alper; Department of Geodetic and Geographical Information Technologies (2017) The aim of this thesis is to investigate the potential of Sentinel-2 satellite for land use and land cover mapping. The commonly known supervised classification algorithms, support vector machines (SVMs) and maximum likelihood classification, are adopted for investigation along with a hierarchical classification model CORINE. The main classes for land cover and mapping are selected as water, vegetation, built-up and bare-land in the first level, which is followed by inland water, marine water, forest/meadow...

Citation Formats

D. C. Demirkan, A. Koz, and H. S. Duzguna, “Hierarchical classification of Sentinel 2-a images for land use and land cover mapping and its use for the CORINE system,” JOURNAL OF APPLIED REMOTE SENSING, pp. 0–0, 2020, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/62753.