Unsupervised GRNN flood mapping approach combined with uncertainty analysis using bi-temporal Sentinel-2 MSI imageries - Qi Zhang; Penglin Zhang; Xudong Hu (International Journal of Digital Earth, 2021)
Floods occur frequently worldwide. The timely, accurate mapping of the flooded areas is an important task. Therefore, an unsupervised approach is proposed for automated flooded area mapping from bi-temporal Sentinel-2 multispectral images in this paper. First, spatial–spectral features of the images before and after the flood are extracted to construct the change magnitude image (CMI). Then, the certain flood pixels and non-flood pixels are obtained by performing uncertainty analysis on the CMI, which are considered reliable classification samples. Next, Generalized Regression Neural Network (GRNN) is used as the core classifier to generate the initial flood map. Finally, an easy-to-implement two-stage post-processing is proposed to reduce the mapping error of the initial flood map, and generate the final flood map. Different from other methods based on machine learning, GRNN is used as the classifier, but the proposed approach is automated and unsupervised because it uses samples automatically generated in uncertainty analysis for model training. Results of comparative experiments in the three sub-regions of the Poyang Lake Basin demonstrate the effectiveness and superiority of the proposed approach. Moreover, its superiority in dealing with uncertain pixels is further proven by comparing the classification accuracy of different methods on uncertain pixels.