Detection of Urban Built-Up Area Change From Sentinel-2 Images Using Multiband Temporal Texture and One-Class Random Forest

Abstract

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.

Keywords

• Built-up area change
• improved one-class random forest (iOCRF)
• multiband temporal texture
• multitemporal data
• pseudo cross multivariate variogram (PCMV)