Distance Weight-Graph Attention Model-Based High-Resolution Remote Sensing Urban Functional Zone Identification

Abstract

The spatial arrangement of land-cover features constitutes different urban functional zone. With the same attributes of the urban functional zone, the land-cover features that make up the functional zone will have similar spatial distribution characteristics. Considering the importance of understanding spatial relationships between land-cover features, the up-bottom hierarchical decomposition and semantic understanding of functional zone are achieved. First, for object convolution neural network (OCNN)-based land cover classification, an equal-area dividing algorithm is proposed to automatically generate convolution kernel position. Second, to extract spatial relationship features of urban land covers, a novel distance weight-graph attention model (DW-GAM) is originally proposed for classifying urban functional zones by comparing the feature similarity of the land cover relationship graph. Third, considering the extreme difficulties in expressing the urban structure characteristic on a single scale, a recursive model that uses an urban road network of different levels to divide multiscale functional zones is built. Finally, taking the analysis of urban function allocation as the application objective, this article establishes a primary framework of the spatial pattern evaluation index. Experimental results conducted on a Google Earth image of Xi'an city show that the multiscale recursive model can accurately recognize urban functional zones by using the originally proposed DW-GAM. Then, based on the outcome of urban functional zone identification, the case study of urban function allocation analysis is innovatively conducted on the fine scale to give some suggestions on future urban planning, which is, hence, of great significance for urban function pattern analysis and urban planning.