![]() ![]() Reliable SMA is critical for the post-sub-pixel mapping process. The spectral mixture analysis (SMA) can be used as a pre-processing step for water mapping at sub-pixel resolution. Among these methods, the first three groups of methods can only obtain water maps at the pixel level, which may not meet the precise requirements of the practical applications in urban areas. The two-band spectral water index method has the dual advantages of simplicity and high precision it is the most frequently used method for water extraction. Single-band thresholding is the simplest water extraction method, it mainly depends on the reflectivity difference of water and others in the infrared wave bands, and it can achieve high precision in most cases. The hard classification method can achieve high precision in the water extraction, however, its algorithm is always complicated and time-consuming. To date, various water body extraction algorithms for optical imagery have been developed, and they can be categorized into four basic types: (1) hard classification (2) single-band thresholding (3) spectral water indexes and (4) spectral unmixing. Water absorbs most of the energy in the near-infrared and middle-infrared wavelengths, whereas other surface materials have a higher reflectance in these wavelengths. Optical remote sensing of water bodies is based on the difference in the spectral reflectance of land and water. ![]() In the quantitative accuracy assessment, the ASWM method shows the best performance in water mapping with the mean kappa coefficient of 0.862, mean producer’s accuracy of 82.8%, and mean user’s accuracy of 91.8% for test regions. The results indicate that the proposed ASWM was able to detect water pixels more efficiency than other unsupervised water extraction methods, and the water fractions estimated by the proposed ASWM method correspond closely to the reference fractions with the slopes of 0.97, 1.02, 1.04, and 0.98 and the R-squared values of 0.9454, 0.9486, 0.9665, and 0.9607 in regression analysis corresponding to different test regions. ![]() One classical water index method (the modified normalized difference water index (MNDWI)), a pixel-level target detection method (constrained energy minimization (CEM)), and two widely used SMA methods (fully constrained least squares (FCLS) and multiple endmember spectral mixture analysis (MESMA)) were chosen for the water mapping comparison in the experiments. As for obtaining the most representative endmembers, we propose an adaptive iterative endmember selection method based on the spatial similarity of adjacent ground surfaces. Secondly, the SMA technique is applied to the mixed land-water pixels for water abundance estimation. Specifically, we first apply a water index for the automatic extraction of mixed land-water pixels, and the pure water pixels that are generated in this process are exported as the final result. The objective of this study is to develop an automatic subpixel water mapping method (ASWM) which can achieve a high accuracy in urban areas. More recently, spectral mixture analysis (SMA) has been widely employed in analyzing the urban environment at the subpixel level. Water indexes are the most common method of water extraction at the pixel level. Remote sensing has increasingly been used for water mapping in rural areas however, when applied to urban areas, this spatially- explicit approach is a challenging task due to the fact that the water bodies are often of a small size and spectral confusion is common between water and the complex features in the urban environment. Water bodies are a fundamental element of urban ecosystems, and water mapping is critical for urban and landscape planning and management. ![]()
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