An Object-Oriented Semi-Supervised Land-Use/Land-Cover Change Detection Method Based on Siamese Autoencoder Graph Attention Network

Change detection via remote sensing data is a popular method for monitoring land cover/land use. Graph attention network (GAT) is a method that can improve the change detection performance of land cover/land use monitoring by enhancing the feature representation of remote sensing images. However, the shortcomings of connection sparsity and insufficient sample feature mining of the GAT affect the application of these methods in change detection. This paper proposes a Siamese autoencoder graph attention networks for object-oriented land cover/land use change detection via high-resolution remote sensing, which is useful for semi-supervised problem methods with poor simplicity. First, we reduce the pressure of graph network model operations and obtain multidimensional features via the growing multilevel segmentation strategy (GMSS). The adjacency matrix is established by adding strongly connected edges via the weighted difference similarity matching method (WDSM) with image objects as nodes. Second, we use the Siamese autoencoder to pretrain the node features and migrate the weight parameters to the GAT feature extraction layer. Finally, a small number of samples are selected to train the GAT and predict all nodes. The experimental results show that the average overall accuracy of the proposed method is 95.41%, and the average F1 score is 89.35%, which are at least 5.04% and 10.84% better than those of other typical methods, such as the graph convolutional network (GCN). In particular, detecting roads and bare soil is significantly better than that of other methods.