Semantic Network-Based Impervious Surface Extraction Method for Rural-Urban Fringe From High Spatial Resolution Remote Sensing Images

• Main Authors: Yongzhi Wang, Qi Huang, Aiguo Zhao, Hua Lv, Shengbing Zhuang
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Subjects: Feature selection; impervious surface; multiscale segmentation; semantic network model; VHSRRSI
• Online Access: https://ieeexplore.ieee.org/document/9427082/

Impervious surfaces, as a key indicator of urban spatial environmental factors, have great significance in exploring the distribution law and spatial pattern of rural–urban fringe areas. To handle the increasingly rich feature information and complicated urban spatial structure in high spatial resolution remote sensing images (HSRRSIs), a semantic network model-guided extraction method for HSRRSI impervious surfaces in rural–urban fringes is proposed. The proposed method mainly includes three parts: First, construction of a semantic network model of ground covers in the rural–urban fringe and dimensionality reduction of its features. Second, optimization of a multi-scale segmentation algorithm based on the estimation of scale parameter 2 method and the fitness function. Third, proposal of a feature reduction method based on the ReliefF feature selection algorithm for spectral, texture, and geometry features to reduce the data redundancy in HSRRSIs. Finally, with the Geoeye-1 image of the rural–urban fringe of Zhanggong District as the data source, CART, RF, and SVM classifiers are used to extract the impervious surfaces of two different areas (named as Q1 and Q2), Q1 comprises the edge of rural–urban fringe with densely distributed industrial plants, and Q2 comprises a rural–urban fringe with a pronounced transition from urban to rural areas. Results show that the highest impervious surface extraction accuracy of the SVM classifier based on the semantic network model is obtained when the segmentation scale is at 210 and 215. The producer accuracy and overall accuracy for Q1 and Q2 are (94.27%, 86.41%) and (94.46%, 89.47%), respectively.