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.
Highlights
Urbanization process, including population growth and urban land expansion, has been given much focus in recent years [1]
To address the aforementioned problems, we proposed a method for detection of urban built-up area change using the multi-band temporal texture measured by pseudo cross multivariate variogram (PCMV) [32] and oneclass classification [39]
The multi-band temporal textures measured by PCMV were computed from Sentinel-2 multispectral images between every two years
Summary
Urbanization process, including population growth and urban land expansion, has been given much focus in recent years [1]. Low and medium resolution satellite images, such as MODIS and Landsat images, have been exploited in detecting and monitoring urban land changes across large areas during the last three decades [1], [10]–[13]. The limitation in spatial resolution of these images makes these data only suitable for detecting urban land expansion in large scales, and are not able to capture urban land changes at fine scales [14]–[16]. Higher spatial resolution data, such as SPOT-5 and Sentinel-2 images with 10-20 m resolutions, are used to monitor urban land expansion at detailed levels [16]–[19]. The increased spatial details, temporal resolution and thematic contents offered by Sentinel-2 data have potential for detecting urban land expansion at fine scale
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