Abstract
Land surface water mapping is one of the most basic classification tasks to distinguish water bodies from dry land surfaces. In this paper, a water mapping method was proposed based on multi-scale level sets and a visual saliency model (MLSVS), to overcome the lack of an operational solution for automatically, rapidly and reliably extracting water from large-area and fine spatial resolution Synthetic Aperture Radar (SAR) images. This paper has two main contributions, as follows: (1) The method integrated the advantages of both level sets and the visual saliency model. First, the visual saliency map was applied to detect the suspected water regions (SWR), and then the level set method only needed to be applied to the SWR regions to accurately extract the water bodies, thereby yielding a simultaneous reduction in time cost and increase in accuracy; (2) In order to make the classical Itti model more suitable for extracting water in SAR imagery, an improved texture weighted with the Itti model (TW-Itti) is employed to detect those suspected water regions, which take into account texture features generated by the Gray Level Co-occurrence Matrix (GLCM) algorithm, Furthermore, a novel calculation method for center-surround differences was merged into this model. The proposed method was tested on both Radarsat-2 and TerraSAR-X images, and experiments demonstrated the effectiveness of the proposed method, the overall accuracy of water mapping is 98.48% and the Kappa coefficient is 0.856.
Highlights
Water resources are an irreplaceable strategic resource for human survival
This paper proposes a fast and reliable land surface water mapping method of large-area and high-resolution Synthetic Aperture Radar (SAR) images
A multi-scale level set method was proposed by Sui et al [25], the difference with our approach being that we use OTSU algorithms [26] to adaptively define the initial level set function; (4) To detect objects that may be confused with calm water, a post-processing method was proposed based on object-oriented geometrical features
Summary
Land surface water (LSW) is an important part of the water cycle. LSW mapping, using remote sensing techniques, plays an important role in wetland monitoring, flood monitoring, flood disaster assessment, surface water area estimation, and water resources management. Multi-spectral optical images have been found to be effective when the sky is clear, there are some limitations in overcast weather conditions. Due to their capabilities of large-area coverage, cloud penetration and all-weather acquisition, the SAR image data could compensate for the weakness in optical images in such conditions for LSW mapping [1]. This paper proposes a fast and reliable land surface water mapping method of large-area and high-resolution SAR images. Calm water was considered including calm rivers and lakes, not including puddles and paddy fields
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