Traditional object-oriented classification methods mostly use spectral features of image objects and ignore the spatial features among image objects. In this paper, an object-oriented classification method for high-resolution remote sensing images using improved inductive graph neural network is proposed. The method is able to adaptively adjust the fusion coefficient of spectral-spatial composite node similarity and automatically determine the optimal sampling number of neighboring nodes. First, we improved the K-nearest neighbor (KNN) graph construction method. The standard deviation informativeness evaluation method was used to determine the fusion coefficients for constructing the composite node similarity of spectral and spatial features. Then, the optimal sampling number of neighboring nodes was determined using the feedback curve method, and feature representation was accomplished using GraphSAGE node embedding. Finally, the classifications of the nodes were predicted by Softmax function. We used GID-15 and BDCI2017 datasets as experimental data. The proposed graph construction method has improved the classification accuracy. The average Kappa coefficient of the proposed method was better than CART, GCN, GAT, LANet, CCTNet, and SLCNet by 0.31, 0.14, 0.13, 0.12, 0.08, and 0.02. The average overall accuracy, on the other hand, was better than 42.31%, 7.4%, 6.73%, 8.69%, 6.03%, and 1.52%. Meanwhile, our method had good robustness in vegetation and built-up land extraction. The method proposed in this paper provides an effective tool for land cover classification of high-resolution remote sensing images.
