Abstract
Given the limited features (for example, the backscattering coefficient threshold range) of single-channel Synthetic Aperture Radar (SAR) images, it is difficult to distinguish ground objects similar to the backscattering coefficients of water bodies. In this paper, two representative research areas are selected (Yancheng Coastal wetland and Shijiu Lake), and the fully polarized SAR data based on Gaofen-3 are used to extract water bodies using the method of polarization decomposition and gray level co-occurrence matrix. Firstly, the multi-dimensional features of ground objects are extracted, and then the redundancy processing of multi-dimensional features is carried out by the separability index, which effectively solves the misclassification of non-water bodies and water bodies and improves the accuracy of water body extraction. The comparison between the results of full-polarization extraction and single-polarization extraction shows that both full-polarization and single-polarization extraction can extract water information, but the extraction accuracy of the full-polarization method can reach 94.74% in the area with complex wetland features, which can effectively compensate for the lack of precision of the single-polarization method. Although multi-dimensional features can be extracted from fully polarimetric SAR data, data redundancy may exist. Therefore, using the Separability index (SI) to process multi-dimensional features can effectively solve the problem of feature redundancy and improve classification accuracy.
Highlights
The rapid and accurate acquisition of water body information is of great significance to research of water resources [1,2,3,4], flood disaster monitoring [4], ecological environmental protection [3,5], and other fields
For fully polarized Synthetic Aperture Radar (SAR) images, polarization decomposition and gray level co-occurrence matrix are used to extract polarization features and texture features, and multidimensional features are processed by the separability index, which can effectively solve the feature redundancy problem
It can be seen that the pixel value of the water area is relatively low, which can be distinguished from other ground objects
Summary
The rapid and accurate acquisition of water body information is of great significance to research of water resources [1,2,3,4], flood disaster monitoring [4], ecological environmental protection [3,5], and other fields. Optical remote sensing technology has become an important technical means to obtain water information due to its spatial and temporal availability and data processing advantages. Optical remote sensing is often susceptible to bad weather, and it is difficult to obtain high-quality images under cloudy conditions. A Synthetic Aperture Radar (SAR) can make full use of its advantages, featuring high resolution, all-weather observation, and strong penetration, etc., and has become an important data source for water information extraction in recent years [5,6]. In 2016, China successfully launched Gaofen-3 (GF-3), a synthetic aperture radar imaging satellite, which has greatly alleviated the shortage of data from civilian SAR satellites. Water extraction by SAR images can be divided into the single polarization mode and the full polarization mode. Lee et al [7] studied the adaptability of the Otsu threshold segmentation method and found that when the target area accounted for more than 30% of the whole image, the OTSU threshold approached the optimal segmentation value; when the target area ratio was reduced to 10%, the segmentation performance of OTSU declined rapidly
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