Abstract
The rapid change and expansion of human settlements raise the need for precise remote-sensing monitoring tools. While some Land Cover (LC) maps are publicly available, the knowledge of the up-to-date urban extent for a specific instance in time is often missing. The lack of a relevant urban mask, especially in developing countries, increases the burden on Earth Observation (EO) data users or requires them to rely on time-consuming manual classification. This paper explores fast and effective exploitation of Sentinel-1 (S1) and Sentinel-2 (S2) data for the generation of urban LC, which can be frequently updated. The method is based on an Object-Based Image Analysis (OBIA), where one Multi-Spectral (MS) image is used to define clusters of similar pixels through super-pixel segmentation. A short stack (<2 months) of Synthetic Aperture Radar (SAR) data is then employed to classify the clusters, exploiting the unique characteristics of the radio backscatter from human-made targets. The repeated illumination and acquisition geometry allows defining robust features based on amplitude, coherence, and polarimetry. Data from ascending and descending orbits are combined to overcome distortions and decrease sensitivity to the orientation of structures. Finally, an unsupervised Machine Learning (ML) model is used to separate the signature of urban targets in a mixed environment. The method was validated in two sites in Portugal, with diverse types of LC and complex topography. Comparative analysis was performed with two state-of-the-art high-resolution solutions, which require long sensing periods, indicating significant agreement between the methods (averaged accuracy of around 90%).
Highlights
In recent decades, mapping urban areas and growth has been a vital tool in facing many environmental challenges
S2GLC is a 10 m Land Cover (LC) map for the year 2017 over Europe, generated using S2 data only [45]. It is published on the CREODIAS platform, and the relevant tile was downloaded for the sake of the analysis presented here
We exploit the potential of combining Synthetic Aperture Radar (SAR) and MS data in the context of an Object-Based Image Analysis (OBIA) classification for urban map generation
Summary
In recent decades, mapping urban areas and growth has been a vital tool in facing many environmental challenges. MS remote sensing instruments [1]. Such data resources are often costly and unavailable to common users. The recent increase in the availability of open-access satellite data has given rise to the need for algorithms that can exploit moderate-resolution images. Obtaining a stack of MS images may be challenging in parts of the world due to weather limitations; the alternative usage of SAR for the recognition of urban areas is widely studied [5,6,7,8,9]. SAR sensors operate in the radio frequency range, which penetrates clouds, and allow regular sampling worldwide
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