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%).

Abstract. The coastal environment is among the most fragile regions on our planet. Its efficient monitoring is crucial to properly manage human and natural resources located in this environment where a large portion of our population lives. The objective of this contribution is to design and develop a new set of methods suitable for detecting and tracking the coastline. Synthetic aperture radar (SAR) technology is chosen because of the characteristic response from water and the acquisition consistency allowed by constant illumination, day-and-night, and all-weather functioning. The proposed iterative detection method is based on superpixel segmentation. The resulting superpixels are filtered and then partitioned in land and water classes based on their median backscattering with Otsu’s algorithm. The rationale is that the segmentation can follow the coastline before the filtering can degrade the spatial resolution. A quantitative assessment of the results measures the distance to a manually-detected shoreline for the Lizard Island case study; the average distance is 12.63 m, with 80% of the sampled points within 20 m. The innovative coastline monitoring process exploits the consistency of SAR by analyzing a long time series. After a season-wise grouping, the land-water index is introduced to erase the time oscillation of water backscattering caused by different sea states. The proposed index is modeled in time on a pixel basis. A visualization technique that exploits the HSV codification of the color space highlights where and when changes happened. A case study for this technique is carried out over the Reentrancias Maranhenses natural area. A quality assessment shows good accordance with optical data that depicts the region’s dynamic.

Abstract. Mapping the exact extent of urban areas is a critical prerequisite in many remote sensing applications, such as hazard evaluation and change detection. The usage of Synthetical Aperture Radar (SAR) data has gained popularity due to the unique characteristics of the backscattered radio signal from human-made targets. The Sentinel-1 (S1) constellation, with a global revisit time of 6–12 days in Interferometric Wide Swath (IW) mode and free and open access to the data, allows the development of new applications to monitor urban sites. However, S1 is rarely considered when fine resolution is required due to the large pixel size and the need for spatial averaging to obtain robust estimators. We propose a method to improve Sentinel-1 urban classification performance by exploiting one Multi-Spectral (MS) image acquired by Sentinel-2 (S2). MS data is used for tracing the precise natural boundaries in a scene through superpixels segmentation. A machine learning approach is then applied to interpret the thematic context of each segment from short temporal stacks of coregistered SAR data. We use a short sensing period (around two months), so rapid changes can be traces. The proposed fusion of S1 and S2 data was tested in the area of Milan (Italy), with a total accuracy of about 90%. The ability to follow high-resolution details in a mixed environment is demonstrated, opening the possibility of efficiently tracing the human footprint.

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The paper proposes a flexible and efficient wavenumber domain processing scheme suited for close formations of low earth orbiting (LEO) synthetic aperture radar (SAR) sensors hosted on micro-satellites or CubeSats. Such systems aim to generate a high-resolution image by combining data acquired by each sensor with a low pulse repetition frequency (PRF). This is usually performed by first merging the different channels in the wavenumber domain, followed by bulk focusing. In this paper, we reverse this paradigm by first upsampling and focusing each acquisition and then combining the focused images to form a high-resolution, unambiguous image. Such a procedure is suited to estimate and mitigate artifacts generated by incorrect positioning of the sensors. An efficient wave–number method is proposed to focus data by adequately coping with the orbit curvature. Two implementations are provided with different quality/efficiency. The image quality in phase preservation, resolution, sidelobes, and ambiguities suppression is evaluated by simulating both point and distributed scatterers. Finally, a demonstration of the capability to compensate for ambiguities due to a small across-track baseline between sensors is provided by simulating a realistic X-band multi-sensor acquisition starting from a stack of COSMO-SkyMed images.