82 research outputs found
Polarimetric SAR for the monitoring of agricultural crops
The monitoring of agricultural crops is a matter of great importance. Remote
sensing has been unanimously recognized as one of the most important techniques for
agricultural crops monitoring. Within the framework of active remote sensing, the
capabilities of the Synthetic Aperture Radar (SAR) to provide fine spatial resolution
and a wide area coverage, both in day and night time and almost under all weather
conditions, make it a key tool for agricultural applications, including the monitoring
and the estimation of phenological stages of crops. The monitoring of crop phenology
is fundamental for the planning and the triggering of cultivation practices, since
they require timely information about the crop conditions along the cultivation
cycle. Due to the sensitivity of polarization of microwaves to crop structure and
dielectric properties of the canopy, which in turn depend on the crop type, retrieval
of phenology of agricultural crops by means of polarimetric SAR measurements is
a promising application of this technology, especially after the launch of a number
of polarimetric satellite sensors.
In this thesis C-band polarimetric SAR measurements are used to estimate pheno-
logical stages of agricultural crops. The behavior of polarimetric SAR observables
at different growth stages is analyzed and then estimation procedures, aimed at the
retrieval of such stages, are defined.
The second topic on which this thesis is focused on is the land cover types discrimi-
nation by means of X-band multi-polarization SAR data
Polarimetric SAR for the monitoring of agricultural crops
The monitoring of agricultural crops is a matter of great importance. Remote
sensing has been unanimously recognized as one of the most important techniques for
agricultural crops monitoring. Within the framework of active remote sensing, the
capabilities of the Synthetic Aperture Radar (SAR) to provide fine spatial resolution
and a wide area coverage, both in day and night time and almost under all weather
conditions, make it a key tool for agricultural applications, including the monitoring
and the estimation of phenological stages of crops. The monitoring of crop phenology
is fundamental for the planning and the triggering of cultivation practices, since
they require timely information about the crop conditions along the cultivation
cycle. Due to the sensitivity of polarization of microwaves to crop structure and
dielectric properties of the canopy, which in turn depend on the crop type, retrieval
of phenology of agricultural crops by means of polarimetric SAR measurements is
a promising application of this technology, especially after the launch of a number
of polarimetric satellite sensors.
In this thesis C-band polarimetric SAR measurements are used to estimate pheno-
logical stages of agricultural crops. The behavior of polarimetric SAR observables
at different growth stages is analyzed and then estimation procedures, aimed at the
retrieval of such stages, are defined.
The second topic on which this thesis is focused on is the land cover types discrimi-
nation by means of X-band multi-polarization SAR data
Oil Spills and Slicks imaged by Synthetic Aperture Radar
Oil spills and slicks occur in the ocean around the world due to natural seeps, oil extraction, transportation, and consumption. Satellite synthetic aperture radar (SAR) has proven to be an efficient tool for identifying and classifying oil on the sea surface. This information can be used to monitor
areas for potential illegal marine discharge or to respond to an oil spill incident. When used to monitor shipping lanes or drilling platforms, timely analysis can identify offending parties and lead to prosecution. Following an oil spill such as that from the Deepwater Horizon rig in the Gulf of Mexico in 2010, SAR can be used to direct response activities and optimize available resources
Exploiting satellite SAR for archaeological prospection and heritage site protection
Optical and Synthetic Aperture Radar (SAR) remote sensing has a long history of use and reached a good level of maturity in archaeological and cultural heritage applications, yet further advances are viable through the exploitation of novel sensor data and imaging modes, big data and high-performance computing, advanced and automated analysis methods. This paper showcases the main research avenues in this field, with a focus on archaeological prospection and heritage site protection. Six demonstration use-cases with a wealth of heritage asset types (e.g. excavated and still buried archaeological features, standing monuments, natural reserves, burial mounds, paleo-channels) and respective scientific research objectives are presented: the Ostia-Portus area and the wider Province of Rome (Italy), the city of Wuhan and the Jiuzhaigou National Park (China), and the Siberian âValley of the Kingsâ (Russia). Input data encompass both archive and newly tasked medium to very high-resolution imagery acquired over the last decade from satellite (e.g. Copernicus Sentinels and ESA Third Party Missions) and aerial (e.g. Unmanned Aerial Vehicles, UAV) platforms, as well as field-based evidence and ground truth, auxiliary topographic data, Digital Elevation Models (DEM), and monitoring data from geodetic campaigns and networks. The novel results achieved for the use-cases contribute to the discussion on the advantages and limitations of optical and SAR-based archaeological and heritage applications aimed to detect buried and sub-surface archaeological assets across rural and semi-vegetated landscapes, identify threats to cultural heritage assets due to ground instability and urban development in large metropolises, and monitor post-disaster impacts in natural reserves
Offshore oil spill detection using synthetic aperture radar
Among the different types of marine pollution, oil spill has been considered as a major threat to the sea ecosystems. The source of the oil pollution can be located on the mainland or directly at sea. The sources of oil pollution at sea are discharges coming from ships, offshore platforms or natural seepage from sea bed. Oil pollution from sea-based sources can be accidental or deliberate. Different sensors to detect and monitor oil spills could be onboard vessels, aircraft, or satellites. Vessels equipped with specialised radars, can detect oil at sea but they can cover a very limited area. One of the established ways to monitor sea-based oil pollution is the use of satellites equipped with Synthetic Aperture Radar (SAR).The aim of the work presented in this thesis is to identify optimum set of feature extracted parameters and implement methods at various stages for oil spill detection from Synthetic Aperture Radar (SAR) imagery. More than 200 images of ERS-2, ENVSAT and RADARSAT 2 SAR sensor have been used to assess proposed feature vector for oil spill detection methodology, which involves three stages: segmentation for dark spot detection, feature extraction and classification of feature vector. Unfortunately oil spill is not only the phenomenon that can create a dark spot in SAR imagery. There are several others meteorological and oceanographic and wind induced phenomena which may lead to a dark spot in SAR imagery. Therefore, these dark objects also appear similar to the dark spot due to oil spill and are called as look-alikes. These look-alikes thus cause difficulty in detecting oil spill spots as their primary characteristic similar to oil spill spots. To get over this difficulty, feature extraction becomes important; a stage which may involve selection of appropriate feature extraction parameters. The main objective of this dissertation is to identify the optimum feature vector in order to segregate oil spill and âlook-alikeâ spots. A total of 44 Feature extracted parameters have been studied. For segmentation, four methods; based on edge detection, adaptive theresholding, artificial neural network (ANN) segmentation and the other on contrast split segmentation have been implemented. Spot features are extracted from both the dark spots themselves and their surroundings. Classification stage was performed using two different classification techniques, first one is based on ANN and the other based on a two-stage processing that combines classification tree analysis and fuzzy logic. A modified feature vector, including both new and improved features, is suggested for better description of different types of dark spots. An ANN classifier using full spectrum of feature parameters has also been developed and evaluated. The implemented methodology appears promising in detecting dark spots and discriminating oil spills from look-alikes and processing time is well below any operational service requirements
Microwave Satellite Measurements for Coastal Area and Extreme Weather Monitoring
In this project report, the main outcomes relevant to the Sino-European Dragon-4 cooperation project ID 32235 âMicrowave satellite measurements for coastal area and extreme weather monitoringâ are reported. The project aimed at strengthening the Sino-European research cooperation in the exploitation of European Space Agency, Chinese and third-party mission Earth Observation (EO) microwave satellite data. The latter were exploited to perform an effective monitoring of coastal areas, even under extreme weather conditions. An integrated multifrequency/polarization approach based on complementary microwave sensors (e.g., Synthetic Aperture Radar, scatterometer, radiometer), together with ancillary information coming from independent sources, i.e., optical imagery, numerical simulations and ground measurements, was designed. In this framework, several tasks were addressed including marine target detection, sea pollution, sea surface wind estimation and coastline extraction/classification. The main outcomes are both theoretical (i.e., new models and algorithms were developed) and applicative (i.e., user-friendly maps were provided to the end-user community of coastal area management according to smart processing of remotely sensed data). The scientific relevance consists in the development of new algorithms, the effectiveness and robustness of which were verified on actual microwave measurements, and the improvement of existing methodologies to deal with challenging test cases
Microwave satellite remote sensing for a sustainable sea
The oceans cover roughly 2/3 of the Earthâs surface and are a fundamental ecosystem regulating climate, weather and representing a huge reservoir of biodiversity and natural resources. The preservation of the oceans is therefore not only relevant on an environmental perspective but also on an economical one. A sustainable approach is requested that cannot be simply achieved by improving technologies but calls for a shared new vision of common goods.Within such a complex and holistic problem, the role of satellite microwave remote sensing to observe marine ecosystem and to assist a sustainable development of human activities must be considered. In such a view the paper is meant. Accordingly, the key microwave sensor technologies are reviewed paying particular emphasis on those applications that can provide effective support to pursue some of the UN Sustainable Development Goals. Three meaningful sectors are showcased:oil and gas, where microwave sensors can provide continuous fine-resolution monitoring of critical infrastructures; renewable energy, where microwave satellite remote sensing allows supporting the management of offshore wind farms during both feasibility and operational stages; plastic pollution, where microwave technologies that exploit signals of opportunity offer large-scale monitoring capability to provide marine litter maps of the oceans
Spaceborne L-Band Synthetic Aperture Radar Data for Geoscientific Analyses in Coastal Land Applications: A Review
The coastal zone offers among the worldâs most productive and valuable ecosystems and is experiencing increasing pressure from anthropogenic impacts: human settlements, agriculture, aquaculture, trade, industrial activities, oil and gas exploitation and tourism. Earth observation has great capability to deliver valuable data at the local, regional and global scales and can support the assessment and monitoring of landâ and waterârelated applications in coastal zones. Compared to optical satellites, cloudâcover does not limit the timeliness of data acquisition with spaceborne Synthetic Aperture Radar (SAR) sensors, which have allâweather, day and night capabilities. Hence, active radar systems demonstrate great potential for continuous mapping and monitoring of coastal regions, particularly in cloudâprone tropical and subâtropical climates. The canopy penetration capability with long radar wavelength enables Lâband SAR data to be used for coastal terrestrial environments and has been widely applied and investigated for the following geoscientific topics: mapping and monitoring of flooded vegetation and inundated areas; the retrieval of aboveground biomass; and the estimation of soil moisture. Human activities, global population growth, urban
sprawl and climate changeâinduced impacts are leading to increased pressure on coastal ecosystems causing land degradation, deforestation and land use change. This review presents a comprehensive overview of existing research articles that apply spaceborne Lâband SAR data for geoscientific
analyses that are relevant for coastal land applications
On the use of multipolarization satellite SAR data for coastline extraction in harsh coastal environments: the case of Solway Firth
This study deals with coastline extraction using multipolarization spaceborne synthetic aperture radar (SAR) imagery acquired over coastal intertidal areas. The latter are very challenging environments where mud flats lead to a large variability of normalized radar cross section, which may trigger a significant number of false edges during the extraction process. The performance of SAR-based coastline extraction methods that rely on a joint combination of multipolarization information (either single- or dual-polarization metrics) and speckle filtering (either local and nonlocal approaches) are analyzed using global positioning system (GPS) samples and colocated SAR imagery collected under different incidence angles. Our test site is an intertidal zone with a wetland (i.e., salt marsh) in the Solway Firth, south-west along the Scottish-English border. Experimental results, obtained processing a pair of RadarSAT-2 full-polarimetric and a pair of Sentinel-1 dual-polarimetric SAR imagery augmented by colocated GPS samples, show that: first, the multipolarization information outperforms the single-polarization counterpart in terms of extraction accuracy; second, among the single-polarization channels, the cross-polarized one performs best; third, both single- and dual-polarization methods perform better when nonlocal speckle filtering is applied; fourth, the joint combination of nonlocal speckle filter and dual-polarization information provides the best accuracy; and finally, the incidence angle plays a role in the extraction accuracy with larger incidence angles resulting in the best performance when dual-polarization metric is used
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