融合高分辨率遥感影像和POI数据的多特征潜在语义信息用于识别城市功能区

Identify Urban Functional Zones Using Multi Feature Latent Semantic Fused Information of High-spatial Resolution Remote Sensing Image and POI Data

准确识别和划分城市功能区对合理规划城市发展和解决城市问题具有重要作用。遥感影像拥有丰富的光谱纹理特征但难以表征建筑物的社会经济属性,而社交媒体数据等城市数据为城市研究与应用提供了丰富的数据资源,补充了遥感影像所缺失的建筑物内在特征。融合高分辨遥感影像和POI数据的多特征信息,利用嵌入主题模型挖掘其潜在语义信息识别城市功能区。以宁波市2个典型的城市商业区为研究区设计3个实验,验证该方法的效果和性能。研究结果表明:该方法能够取得85.67%和85.78%的分类精度,并准确识别出城市功能区。同时,光谱、纹理、几何和POI特征组合的多特征信息能够明显提升城市功能区的识别精度,并且嵌入主题模型能够更好地挖掘多特征的高层次潜在语义信息,效果明显优于pLSA、LDA和STM 3种主流模型。

Accurate identification and division of urban functional zones play an important role in rational planning of urban development and solving urban problems. Remote sensing images have rich spectral texture features,but it is difficult to characterize the social and economic attributes of buildings,while urban data such as social media data provide rich data resources for urban research and application,and supplement the internal characteristics of buildings missing from remote sensing images. In this study,multi-feature information of high-resolution remote sensing image and POI data is integrated and embedded topic model is used to mine its potential semantic information to identify urban functional areas. Three experiments were designed with two typical urban business districts in Ningbo as the study area to verify the effect and performance of the research method. The results show that this method can achieve 85.67% and 85.78% classification accuracy,and can accurately identify urban functional zones. At the same time,the multi-feature information of spectral,texture,geometry and POI feature combination can significantly improve the identification accuracy of urban functional zones,and the embedded topic model can mine the high-level potential semantic information of multi-features better than the three mainstream topic models of pLSA,LDA and STM.