当前位置：
X-MOL 学术
›
IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.
›
论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Detection of Urban Built-Up Area Change From Sentinel-2 Images Using Multiband Temporal Texture and One-Class Random Forest
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( IF 5.5 ) Pub Date : 2021-06-24 , DOI: 10.1109/jstars.2021.3092064 Xiaoxue Feng , Peijun Li , Tao Cheng
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( IF 5.5 ) Pub Date : 2021-06-24 , DOI: 10.1109/jstars.2021.3092064 Xiaoxue Feng , Peijun Li , Tao Cheng
Detection of urban land expansion is important for understanding the urbanization process and improving urban planning. Spatio-temporal contextual information derived from multitemporal high-resolution imagery is useful for highlighting urban land cover changes. This article proposes a new method for detecting urban built-up area change from multitemporal high spatial resolution imagery by combining spectral and spatio-temporal features. A multiband temporal texture measured using pseudo cross multivariate variogram (PCMV) is adopted to quantify the local spatio-temporal dependence between bitemporal multispectral images. The PCMV textures at multiple scales, bitemporal spectral features, and normalized difference vegetation indices are together input to an improved one-class random forest classifier for urban built-up area change mapping. The proposed method is evaluated in urban built-up area change detection using multitemporal Sentinel-2 images of Tianjin area acquired from 2015 to 2019. It is also compared with three feature combinations and an existing postclassification comparison method based on one-class support vector machine. Experimental results demonstrate that the proposed method outperformed the traditional ones, with increases of 2.15%–7.38%, 2.07%–5.45%, 1.93%–6.76%, and 5.98%–13.11% in overall accuracy. Moreover, the proposed method also achieves the best performance using the bitemporal Sentinel-2 images over the east of Beijing area. The proposed method is promising as a simple and reliable way to detect urban built-up area change with multitemporal Sentinel-2 imagery.
更新日期：2021-07-27
京公网安备 11010802027423号