Abstract
Remote sensing techniques has been widely used for detecting water bodies in especially wetlands. Different classification methods and water indices has used for this purpose and there are numerous studies for detecting water bodies. However, detecting shallow water area is difficult comparing with deep water bodies because of the mixed pixels. Akgol Wetland is chosen as study area to detect shallow water. For this purpose, Sentinel 2 satellite image, which gives more accurate results thanks to higher spatial resolution than the images having medium spatial resolution, is used. In this study, two classification approaches were applied on Sentinel 2 image to detect shallow water area. In the first approach, effectiveness of indices was determined and classification of spectral bands with indices shows higher accuracy than classification of only spectral bands by using support vector machine classification method. In the second approach, support vector machine recursive feature elimination method used for the most effective features in the first approach. Besides overall accuracy of only spectral bands is obtained as 88.10%, spectral bands and indices' accuracy was obtained as 91.84%.
Original language | English |
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Pages (from-to) | 1269-1273 |
Number of pages | 5 |
Journal | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
Volume | 42 |
Issue number | 2/W13 |
DOIs | |
Publication status | Published - 4 Jun 2019 |
Event | 4th ISPRS Geospatial Week 2019 - Enschede, Netherlands Duration: 10 Jun 2019 → 14 Jun 2019 |
Bibliographical note
Publisher Copyright:© Authors 2019.
Funding
The authors would like to express their thanks to the National Scientific and Technological Research Council of Turkey (TUBITAK) for their financial support to Project number 116Y142.
Funders | Funder number |
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TUBITAK | 116Y142 |
Consejo Nacional para Investigaciones Científicas y Tecnológicas |
Keywords
- Feature selection
- Machine learning
- Remote sensing
- Shallow water
- SVM
- Water indices