An effective superpixel-based graph convolutional network for small waterbody extraction from remotely sensed imagery

Small waterbodies sustain susceptible ecosystems and are influenced by variable dynamics associated with human activities and environmental disturbances. Although remote sensing has displayed efficiency in mapping surface waterbodies on a regular basis, the identification of small waterbodies such as ponds or irrigation ditches remains a challenge, as small waterbodies are often confused with other low-reflectivity surfaces. In this study, a superpixel-based graph convolutional network (GCN) for small waterbody extraction (SG-waterNet) is proposed. Specifically, the SG-waterNet method includes a new object-based representation of an image called a superpixel graph. The superpixel graph contains compact spectral and contextual information and can be exploited by the GCN. A deep GCN architecture is used to efficiently preserve small waterbody features and detect surface waterbodies with high completeness and correctness. We tested the proposed approach on a frequently used open-access Gaofen Image Dataset (GID) and Gaofen-1 image from Hubei Province in China (a total of 11,660 km(2) for research). The extraction accuracy of SG-waterNet for small waterbodies (< 2 ha) was between 84.31% and 89.77% at the five evaluation sites, and the method extracted waterbodies 300 m2 and larger with high confidence. Compared with six state-of-the-art methods, SG-waterNet exhibited significant sensitivity to small waterbodies (especially smaller than 100 m(2)) and detected small waterbody boundaries with the highest completeness and correctness. The average accuracy improvement achieved with SG-waterNet at the evaluation sites ranged from 11.10% to 13.87%. The proposed method is a significant advancement in small waterbody monitoring and can provide promising and practical solutions for real-world applications.