A Novel Wetland Classification Method Combined CNN and SVM Using Multi-Source Remote Sensing Images

Efficient and accurate wetland monitoring is of great significance for controlling climate, preventing floods, and maintaining ecological balance. Due to the characteristics of complex wetland features, there are still problems in feature extraction and classifier selection when dealing with wetland mapping using remote sensing data. In this paper, a novel wetland classification method based on Convolutional Neural Network (CNN) and Support Vector Machine (SVM) is proposed. Firstly, multi-source images are constructed by sentinel-1 and 2. Furthermore, deep features are extracted from multi-source images based on pre-trained CNN. Finally, considering the advantages of SVM in remote sensing classification, the softmax is replaced by L2-SVM. To verify the effectiveness of the proposed method, Qilihai Wetland is used in our experiment. Experimental results show that combining multi-source remote sensing images significantly improves wetland classification accuracy. Moreover, the proposed method has superior performance with OA and Kappa of up to 90.3% and 0.870, especially in small sample categories and complex land-cover types.