DETECTION OF SHALLOW WATER AREA WITH MACHINE LEARNING ALGORITHMS

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Yagmur, N.^1¹  Musaoglu, N.^1² 

[1]ITU, Civil Engineering Faculty, Department of Geomatics Engineering 34469 Maslak Istanbul, Turkey^1

[2]ITU, Institute of Earthquake Engineering and Disaster Management, 34469 Maslak Istanbul, Turkey^2

关键词: Shallow water;    Remote sensing;    Machine learning;    Water indices;    SVM;    Feature selection;   

DOI  :  10.5194/isprs-archives-XLII-2-W13-1269-2019

学科分类：地球科学(综合)

来源: Copernicus Publications

【 摘 要 】

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%.

【 授权许可】

CC BY