GCU-Net: Remote Sensing Classification Method for Coral Reef Geomorphology Integrating Geospatial Cognition

Coral reef is a typical marine ecosystem and has significant implications for protecting marine biodiversity, and maintaining marine ecological balance. Accurate geomorphic information is the base of coral reef conservation, which usually is extracted by high-resolution remote sensing. Recent classification methods for coral reef geomorphology always focus on the extraction of deep spectral and texture features, ignoring the inherent geospatial information of geomorphology and losing the shallow-layer information, which leads to low classification accuracy. This paper proposes a deep learning classification method for coral reef geomorphology, named as GCU-Net which integrates the convolutional attention mechanism and the geo-spatial cognition. Experiments were carried out in North Reef and Zhaoshu Island geomorphology with the Gaofen-2 (GF-2) satellite image. The results demonstrate that the GCU-Net's accurate classification with an overall accuracy (OA) of 90.46% and 88.92%, respectively, which better extracts the useful information in the shallow-layer, and effectively reduces the omission and misclassification of geomorphic types due to the different spatial positions. Our method exhibits excellent classification performance with an OA improvement of over 7% compared to the comparison method. Therefore, the method proposed in this paper is more effective in obtaining accurate information on coral reef geomorphology and can provide technical support for carrying out large-scale fine monitoring of coral reefs.