Abstract
Timely identifying and detecting water bodies from SAR images are significant for flood monitoring and water resources management. In recent decades, deep learning has been applied to water extraction but is subject to the large difficulty of acquiring SAR dataset of various water bodies types, as well as heavy labeling work. In addition, the traditional methods mostly occur over the large, open lakes and rivers, rarely focusing on complex areas such as the urban water, and cannot automatically acquire the classification threshold. To address these issues, a novel water extraction method is proposed with high accuracy in this paper. Firstly, a multiscale feature extraction using a Gabor filter is conducted to reduce the noise and roughly identify water feature. Secondly, we apply the Otsu algorithm as well as a voting strategy to initially extract the homogeneous regions and for subsequent Gaussian mixture model (GMM). Finally, the dual threshold is obtained from the fitted Gaussian distribution of water and non-water, which is integrated into the graph cut model to redefine the weights of the edges, then constructing the energy function of the water map. The dual-threshold graph cut (DTGC) model precisely pinpoints the water location by minimizing the energy function. To verify the efficiency and robustness, our method and comparison methods, including the IGC method and IACM method, are tested on six different types of water bodies, by performing the accuracy assessment via comparing outcomes with the manually labeled ground truth. The qualitative and quantitative results show that the overall accuracy of our method for the whole dataset all surpasses 99%, along with an obvious improvement of the Kappa, F1-score, and IoU indicators. Therefore, DTGC method has the absolute advantage of automatically capturing water maps in different scenes of SAR images without specific prior knowledge and can also determine the optimal threshold range.
Highlights
We find that every synthetic aperture radar (SAR) image has a threshold for roughly identifying water bodies that are determined by analyzing the histogram of SAR image intensity [28]
GF-3 and TerraSAR-X use the raw SAR intensity image with the preprocessing in Figure 1, and only the Sentinel-1 data are performed by terrain correction, which does not affect the experimental results
The water that is not completely detected by the two methods is because the estimation of Gaussian mixture model (GMM) parameters and initial boundary leads to bad decision making
Summary
Water resources have played an indispensable role in human agriculture and industrial production and life. The optical image has a high resolution, capable of providing detailed and visible band information characteristics, which has been exploited for the water body extraction [2]. An example of graph cut segmentation a simple. 3. An3.example of graph cut segmentation for afor simple. The former includes source s and sink t that, respectively, represent the background and. We improve the weight setting method in [36] by combining the dual foreground of the image. In the latter, each pixel p( p ∈ P) represents a node. The V is set threshold for better classification performance, with the edge weight type and its weight as suggested in [36]: setting conditions depicted in Table 1 that are based on the following ideas:
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