Abstract
The objective of this research was to investigate the impact of seasonality on urban land-cover mapping and to explore better classification accuracy by using multi-season Sentinel-1A and GF-1 wide field view (WFV) images, and the combinations of both types of images in subtropical monsoon-climate regions in Southeast China. We obtained multi-season Sentinel-1A and GF-1 WFV images, as well as the combinations of both data, by using a support vector machine (SVM) and a random forest (RF) classifier. The backscatter intensity, texture, and interference-coherence images were extracted from Sentinel-1A images, and different combinations of these Sentinel-1A-derived images were used to evaluate their ability to map urban land cover. The results showed that the performance of winter images was better than that of any other season, while the summer images performed the worst. Higher classification accuracy was achieved by using multi-season images, and satisfactory classification results were obtained when using Sentinel-1A images from only three seasons. The best classification result was achieved using a combination of all Sentinel-1A data from all four seasons and GF-1 WFV data from winter, with an overall accuracy of up to 96.02% and a kappa coefficient reaching 0.9502. The performance of textures was slightly better than that of the backscatter-intensity images. Although the coherence data performed the worst, it was still able to distinguish urban impervious surfaces well. In addition, the overall classification accuracy of RF was better than that of SVM.
Highlights
Urbanization is one of the most dynamic processes in global land-use change [1,2] and a major force in determining land-use and land-cover change [3]
For WAT, the best performance for random forest (RF) was found in the winter Sentinel-1A image (F1 measure = 86.08%), whereas for support vector machine (SVM) it was found in the autumn Sentinel-1A image (F1 measure = 82.84%); the poorest performance came from the spring Sentinel-1A image
When using Sentinel-1A images from only three seasons, the F1 measure of each urban land cover was higher than 85%, with an overall accuracy of up to 90.67% and a kappa coefficient of up to 0.8828
Summary
Urbanization is one of the most dynamic processes in global land-use change [1,2] and a major force in determining land-use and land-cover change [3]. The accelerated urbanization process has negative impacts on the current economy and environment, such as global warming, traffic congestion, the deterioration of urban ecological environments, and so on. Optical remote sensing imagery is one of the most commonly used data sources for LULC change, and is widely used in urban mapping [4,5,6,7]. Because optical remote sensing is susceptible to the effects of cloudy and rainy weather, accurate mapping using optical images is limited. It has been demonstrated that by using all-weather, day-and-night imaging, as well as canopy penetration and high-resolution capabilities [8,9,10], Synthetic Aperture Radar (SAR) images effectively overcome these limitations in land-cover classification
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