Abstract
To obtain accurate information in a timely manner on built-up areas (BAs) is essential for urban planning and natural hazard (e.g., earthquakes) response strategies. In this paper, a new method for BAs extraction using the Sentinel-1 SAR is proposed, which includes two steps: (1) Candidate BAs are first selected as seeds from images that show high backscattering and obvious textural patterns, as characterized by image intensity, Getis-Ord index, and the variogram texture features; (2) region growing is iteratively implemented from these seed pixels to extract the BAs. Sentinel-1 data, with 5 × 20 m2 resolution, are selected over eight cities with various environmental settings around China, to validate the robustness of the proposed method. The results show that the proposed method achieves higher detection accuracy and fewer commission errors compared with the intensity-based region growing and thresholding methods. An averaged accuracy of 96.5% in validation points of eight cities was achieved, which outperforms the GlobCover urban product in both urban and rural area, while fewer commission errors were achieved compared to Landsat data-based methods. Moreover, two polarizations (VV/VH) and the averaged channel are compared for BAs extraction in areas with various environments. It turns out that improved results can be achieved using the averaged image of two polarizations in north China, while the VV image is better suited for BAs extraction in south. These findings indicate that operational BAs mapping over China, and even globally, is possible, since the Sentinel-1 data can provide images with global coverage.
Highlights
It is estimated that up to 66% of the world’s population will live in urban areas by 2050
Under the same slope threshold in the flat urban area, the results reveal that the mask result using SRTM could retain the built-up areas well, but that the ASTER GDEM2 may mask off the BAs
Unlike traditional feature thresholding methods, or only intensity-based methods, spatial indicator and texture feature together with intensity are introduced in the seeds selection procedure for region growing, in order to obtain a BAs map
Summary
It is estimated that up to 66% of the world’s population will live in urban areas by 2050. Landsat and SPOT satellite images were successfully used to monitor historical land cover changes [6,7,8], and project future patterns of urban development in metropolitan regions [7]. Synthetic aperture radar (SAR) systems can work during both day and night and under all weather conditions. This yields more reliable SAR data in perennially cloudy and rainy areas compared to optical images. A status report on the application of SAR for settlement detection, population estimation, assessment of the impact of human activities on the physical environment, mapping and analyzing urban land use patterns, and interpretation of socioeconomic characteristics has been published [12]. In terms of SAR-based BAs-extraction methods, texture measures [20], contextual information [21], local indicators of spatial association (L.I.S.A) [22], support vector machine (SVM) [23], neural network [24,25], and knowledge-based [26] approaches have been investigated with varying levels of success
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