WETLAND MAPPING BY JOINTLY USE OF CONVOLUTIONAL NEURAL NETWORK AND GRAPH CONVOLUTIONAL NETWORK

In Canada, wetlands cover a large extent of landscape, providing necessary services to ecosystems. Wetland mapping and monitoring utilizing satellite Earth Observation (EO) imagery are of great importance for natural resource management. This study established a two-stream deep learning framework based on a Convolutional Neural Network (CNN) and Graph Convolutional Network (GCN) for wetland classification. The proposed architecture includes a dedicated information source for each stream, wherein Sentinel-1 and Sentinel-2 that are independently fed to GCN and CNN networks, respectively. The final classification result is achieved by concatenating the features extracted by each stream. The study area is a part of the Avalon site located on the Island of Newfoundland in Canada, where we have collected ground truth data for different wetland types. The result was compared to that obtained using Random Forest (RF) and Support Vector Machine (SVM) algorithms. The overall accuracies were 87.68%, 85.52%, and 83.7% for the proposed method, the RF, and the SVM, respectively.