Ulva Prolifera subpixel mapping with multiple-feature decision fusion

The unavoidable nature of Ulva prolifera mixed pixel in low-resolution remote sensing images would result in rough boundary of U. prolifera patches, omission of tiny patches, and overestimation of coverage area. The decomposition of U. prolifera mixed pixel addresses the issue of coverage area overestimation, and the remaining problems can be alleviated by subpixel mapping (SPM). Due to the drift and dissipation of U. prolifera, a suitable SPM method is the single image-based unsupervised method. However, the method has difficulties in detail reconstruction, insufficient learning of spectral information, and SPM error introduced by abundance deviation. Therefore, we proposed a multiple-feature decision fusion SPM (MFDFSPM) method. It involves three branches to obtain the spatial, abundance, and spectral features of U. prolifera while considers multi-feature information using the fusion strategy. Experiments on the Geostationary Ocean Color Imager images in the Yellow Sea of China indicate that the MFDFSPM overperforms several typical U. prolifera SPM methods in higher accuracy and stronger robustness in both SPM and abundance calculation, which produced subpixel map with more detailed spatial information and less noise.