1,721,362 research outputs found
Improving Reliability in Nonlinear Hyperspectral Unmixing by Multidimensional Structural Optimization
No full textMapping vegetation in urban areas using Sentinel-2
No full textThe rapid expansion of cities globally leads to new challenges related to quality of life and health. The presence and fractional distribution of vegetation within urban cities directly impact the life and health of urban dwellers. This paper presents an approach to map urban vegetation from Sentinel-2 data. The twin Sentinel-2 satellites offer a 5-day revisit time global coverage at unprecedented spatial and temporal resolution. The temporal resolution allows for seasonal aggregation of the input data, thus providing phenological information. By considering seasonally aggregated Normalized Difference Spectral Vector (NDSV), a classification was performed using Random Forest (RF) and compared with Classification and Regression Trees (CART) and Support Vector Machines (SVM)
Bi- and three-dimensional urban change detection using sentinel-1 SAR temporal series
No full textUrban areas are subject to multiple and very different changes, in a two- and three-dimensional sense, mostly as a consequence of human activities, such as urbanization, but also because of catastrophic and sudden events, such as earthquakes, landslides, or floods. This paper aims at designing a procedure able to cope with both types of changes by combining interferometric coherence and backscatter amplitude, and provide a semantically meaningful analysis of the changes detected in both city inner cores and suburban areas. Specifically, this paper focuses on detecting multi-dimensional changes in urban areas using a stack of repeat-pass SAR data sets from Sentinel-1A/B satellites. The proposed procedure jointly exploits amplitude and coherence time series to perform this task. SAR amplitude is used to extract changes about the urban extents, i.e. in 2D, while interferometric coherence is sensitive to the presence of buildings and to their size, i. e. to 3D changes. The proposed algorithm is tested using a time-series of two years of Sentinel-1 data, from May 2016 to October 2018, and in two different Chinese cities, Changsha and Hangzhou, with the aim to understand both the temporal evolution of the urban extents, and the changes within what is constantly classified as “urban” throughout the considered time period
Structural optimization for accurate characterization of urban areas in hyperspectral datasets
No full textAccurately estimating the urbanization process is a key-factor for the actual implementation of the sustainable development goals identified by transnational institutions and agencies. In order to retrieve precise characterization of the anthropogenic extents and a sound human-environment interaction assessment, the analysis of Earth observations (EOs) plays a crucial role. Especially, the use of nonlinear spectral investigation can improve the description of geometrically and morphologically complex scenes, so that anthropogenic settlements and dynamics can be properly outlined. In this paper, we propose a novel method for directly assessing the distribution of materials and elements in hyperspectral images by means of a structural optimization approach. Experimental results show how the proposed approach is able to deliver accurate and reliable characterization of urban materials and extents
Global Vegetation Mapping for ESA Climate Change Initiative Project Leveraging Multitemporal High Resolution Sentinel-1 SAR Data
No full textThe European Space Agency (ESA) Climate Change Initiative (CCI) is aiming, in its current phase, at an accurate description and analysis of land cover (LC) and land cover change (LCC) using high spatial resolution Earth Observation (EO) data. A new high resolution LC map could have a key role in the extraction of the so-called Essential Climate Variables (ECV), and be crucial to understand climate change. Indeed, until now these important variables have been derived by the climate modelling community at the global scale using medium resolution EO data (i.e., with a spatial sampling between 100 and 300 m)
Inland Water Body Mapping Using Multitemporal Sentinel-1 SAR Data
No full textClimate change studies require increasingly detailed information on land cover and land use, to precisely model and predict climate based on their status and changes. A fundamental land cover type that needs to be constantly monitored by the climate change community is water, but currently there is a lack of high-resolution water body maps at the global scale. In this article, we present a fully automated procedure for the extraction of fine spatial resolution (10 m) inland water land cover maps for any region of the Earth by means of a relatively simple k-means clustering model applied to multitemporal features extracted from Sentinel-1 SAR sequences. Indeed, due to heavy cloud coverage conditions in many locations, multispectral sensors are not suitable for global water body mapping. For this reason, in this work, we deal only with SAR data, and specifically with multitemporal Sentinel-1 data sequences. The experimental results, obtained for three geographical areas selected because of their wide diversity in terms of geomorphology and climate, show an almost complete consistency with existing datasets, and improve them thanks to their finer spatial details
Jointly exploiting sentinel-1 and sentinel-2 for urban mapping
No full textThis work introduces two feature fusion techniques that exploit previously developed algorithms for urban extent extraction from multispectral and SAR spaceborne data, adapting them to the joint use of Sentinel-1 (S1) and Sentinel-2 (S2) data sets. The approaches aim at exploiting the finer spatial and spectral resolution of multispectral S2 data as well as the double bounce backscatter effect that is common to all built-up areas in SAR S1 data. To this aim, we introduce first a simplified and less computational demanding version of the Urban Extractor (UEXT) algorithm, recently introduced for urban extent extraction from S1 data, and improve its results by two different ways of selecting the seed pixels involved in UEXT by means of the urban extent maps extracted from S2 using the normalized difference spectral vector (NDSV), whose application for national and regional extraction of human settlements have already proved as very effective. Experimental results for Rio de Janeiro and Beijing show the improvements obtained by considering one of the two proposed techniques, and explains while the other one fails in achieving similar results
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