Extracting rural impervious surface from LANDSAT 8 OLI imagery using K-Nearest neighbor algorithm

https://tapchi.humg.edu.vn/en/archives?article=1144
  • Affiliations:

    Khoa Trắc địa - Bản đồ và Quản lý đất đai, Trường Đại học Mỏ - Địa chất, Việt Nam

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  • Received: 15th-Aug-2017
  • Revised: 18th-Oct-2017
  • Accepted: 30th-Oct-2017
  • Online: 30th-Oct-2017
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Abstract:

The Impervious surface area in rural areas are difficult to extract from satellite imagery, especially for medium-resolution images such as Landsat. There have been many studies using image classification algorithms based on the basis of Pixel-based values. However, the problems are the estimation of errors during the time of classification of each pixel. The main contribution of this study is that it utilizes of K-Nearest Neighbor (K-NN) algorithm with Landsat 8 OLI imagery to detect a rural impervious surface area in Giao Thuy district. This paper discusses the uses of K-NN rules and its error estimation for classification of each object images in the medium spatial image. In order to achieve the best accuracy using the K-NN algorithm, the standard samples need to have the following criteria: 1. the sample size is large enough, 2. the distribution of samples is in the study area, 3. Maximum separation between standard sets. Results showed that K-NN algorithm was enough accurate for practical applicability for mapping rural impervious surface areas.

How to Cite
Le, H.Thu Thi, Pham, L.Thi, Nguyen, T.Van and La, H.Phu 2017. Extracting rural impervious surface from LANDSAT 8 OLI imagery using K-Nearest neighbor algorithm (in Vietnamese). Journal of Mining and Earth Sciences. 58, 5 (Oct, 2017).

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