Study on urban surface water extraction from heterogeneous environments using GF-2 remotely sensed images

Abstract

The water index can suppress background noise and increase the separability of surface water. Thus, it has been widely used for surface water extraction. Traditional FCM clustering algorithm considers the uncertainty of ground objects without neighborhood spatial information, which is sensitive to background heterogeneity. On the basis of the shortcomings of traditional FCM clustering algorithms, this study proposed a regional FCM clustering algorithm and applied it to extract city surface water in complex environment regions using GF-2 remote sensing imagery. The main steps of the method include (1)Calculating the normalized difference water index after the removal of shadows; (2) Presenting a regional FCM clustering algorithm;(3)Proposing the urban surface water automatic extraction algorithm by combining the water body index and the regional FCM clustering algorithm. Finally, the proposed method was carried out on two GF-2 high-resolution remote sensing image data located in Guangzhou and Wuhan. The experimental results showed that the proposed method has better accuracy and water boundary than state-of-the-art methods. The proposed method also retains regional integrity and local details of surface water objects while effectively inhibiting noise from urban surface water in the complex background, thereby reducing the "salt and pepper" phenomenon found in traditional FCM clustering algorithm.