Jingwen Yuan, Shugen Wang, Chen Wu, and Yonghao Xu
Landscape mapping and pattern analysis of fine-grained urban functional zones (UFZ) are of great research value in the field of urban planning and urban development assessment. Previous studies employing multispectral images have largely enabled only coarse-grained land-use classification of urban areas. By contrast, hyperspectral images can provide very detailed spectral features and have shown some potential to facilitate fine-grained classification of urban areas. In this article, we evaluate and analyze the classification of fine-grained UFZ in the central city of Wuhan, Hubei, China, using GaoFen-5 (GF-5) hyperspectral satellite imagery. We first compare the performance of hyperspectral data (GF-5) and classical multispectral data (Landsat 8) for the classification of fine-grained functional zones of cities by employing two classical classification algorithms and two deep learning methods. We also propose the deep learning-based spectral-spatial unified networks combined with a fully connected conditional random field (SSUN-CRF) algorithm for fine-grained UFZ mapping to enable better landscape pattern analysis. We then analyze the landscape pattern of the main urban areas of Wuhan by combining ten landscape indicators based on the precise UFZ classification results. Experimental results illustrate the followings. First, compared to multispectral images, hyperspectral images can allow for a more accurate UFZ classification. Second, deep learning classification algorithms can better exploit hyperspectral image data, with the SSUN-CRF algorithm, in particular, being able to achieve an overall accuracy of 93.86% and a Kappa coefficient of 92.08%. Finally, the landscape pattern analysis demonstrates that hyperspectral remote sensing imagery shows significant potential in mapping fine-grained UFZ. It is beneficial for further urban study and planning.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 3972 - 3991, 2022-05-12.