An Urban Land Cover Classification Method Based on Segments’ Multidimension Feature Fusion

Abstract

Using object-based deep learning for the urban land cover classification has become a mainstream method. This study proposed an urban land cover classification method based on segments’ object features, deep features, and spatial association features. The proposed method used the synthetic semivariance function to determine the hyperparameters of the superpixel segmentation and subsequently optimized the image superpixel segmentation result. A convolutional neural network and a graph convolutional neural network were used to obtain segments’ deep features and spatial association features, respectively. The random forest algorithm was used to classify segments based on the multidimension features. The results showed that the image superpixel segmentation results had the significant impact on the classification results. Compared with the pixel-based method, the segment-based methods generally yielded the higher classification accuracy. The strategy of multidimension feature fusion can combine the advantages of each single-dimension feature to improve the classification accuracy. The proposed method utilizing multidimension features was more efficient than traditional methods used for the urban land cover classification. The fusion of segments’ object features, deep features, and spatial association features was the best solution for achieving the urban land cover classification.

Keywords

• Graph convolutional neural network (GCN)
• high spatial resolution remote sensing image
• land cover classification
• segments’ multidimension features
• superpixel