213 research outputs found
The Sentinel-1 mission for the improvement of the scientific understanding and the operational monitoring of the seismic cycle
We describe the state of the art of scientific research on the earthquake cycle based on the analysis of Synthetic Aperture Radar (SAR) data acquired from satellite platforms. We examine the achievements and the main limitations of present SAR systems for the measurement and analysis of crustal deformation, and envision the foreseeable advances that the Sentinel-1 data will generate in the fields of geophysics and tectonics. We also review the technological and scientific issues which have limited so far the operational use of satellite data in seismic hazard assessment and crisis management, and show the improvements expected from Sentinel-1 dat
Hierarchical Markov random fields for high resolution land cover classification of multisensor and multiresolution image time series
International audienc
Machine Learning and Pattern Recognition Methods for Remote Sensing Image Registration and Fusion
In the last decade, the remote sensing world has dramatically evolved. New types of sensor, each one collecting data with possibly different modalities, have been designed, developed, and deployed. Moreover, new missions have been planned and launched, aimed not only at collecting data of the Earth's surface, but also at acquiring planetary data in support of the study of the whole Solar system. Indeed, such a variety of technologies highlights the need for automatic methods able to effectively exploit all the available information. In the last years, lot of effort has been put in the design and development of advanced data fusion methods able to extract and make use of all the information available from as many complementary information sources as possible. Indeed, the goal of this thesis is to present novel machine learning and pattern recognition methodologies designed to support the exploitation of diverse sources of information, such as multisensor, multimodal, or multiresolution imagery. In this context, image registration plays a major role as is allows bringing two or more digital images into precise alignment for analysis and comparison. Here, image registration is tackled using both feature-based and area-based strategies. In the former case, the features of interest are extracted using a stochastic geometry model based on marked point processes, while, in the latter case, information theoretic functionals and the domain adaptation capabilities of generative adversarial networks are exploited. In addition, multisensor image registration is also applied in a large scale scenario by introducing a tiling-based strategy aimed at minimizing the computational burden, which is usually heavy in the multisensor case due to the need for information theoretic similarity measures. Moreover, automatic change detection with multiresolution and multimodality imagery is addressed via a novel Markovian framework based on a linear mixture model and on an ad-hoc multimodal energy function minimized using graph cuts or belied propagation methods. The statistics of the data at the various spatial scales is modelled through appropriate generalized Gaussian distributions and by iteratively estimating a set of virtual images, at the finest resolution, representing the data that would have been collected in case all the sensors worked at that resolution. All such methodologies have been experimentally evaluated with respect to different datasets, and with particular focus on the trade-off between the achievable performances and the demands in terms of computational resources. Moreover, such methods are also compared with state-of-the-art solutions, and are analyzed in terms of future developments, giving insights to possible future lines of research in this field
Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
The importance of snow cover extent (SCE) has been proven to strongly link with various
natural phenomenon and human activities; consequently, monitoring snow cover is one the most
critical topics in studying and understanding the cryosphere. As snow cover can vary significantly
within short time spans and often extends over vast areas, spaceborne remote sensing constitutes
an efficient observation technique to track it continuously. However, as optical imagery is limited
by cloud cover and polar darkness, synthetic aperture radar (SAR) attracted more attention for its
ability to sense day-and-night under any cloud and weather condition. In addition to widely applied
backscattering-based method, thanks to the advancements of spaceborne SAR sensors and image
processing techniques, many new approaches based on interferometric SAR (InSAR) and polarimetric
SAR (PolSAR) have been developed since the launch of ERS-1 in 1991 to monitor snow cover under
both dry and wet snow conditions. Critical auxiliary data including DEM, land cover information,
and local meteorological data have also been explored to aid the snow cover analysis. This review
presents an overview of existing studies and discusses the advantages, constraints, and trajectories of
the current developments
Harvest monitoring of Kenyan tea plantations with X-band SAR
Tea is an important cash crop in Kenya, grown in a climatically restricted geographic area where climatic variability is starting to affect yield productivity levels. This paper assesses the feasibility of monitoring tea growth between, but also within fields, using X-band COSMO-SkyMed SAR images (five images at VV polarization and five images at HH polarization). We detect the harvested and nonharvested areas for each field, based on the loss of interferometric coherence between two images, with an accuracy of 52% at VV polarization and 74% at HH polarization. We then implement a normalization method to isolate the scattering component related to shoot growth and eliminate the effects of moisture and local incidence angle. After normalization, we analyze the difference in backscatter between harvested and nonharvested areas. At HH polarization, our backscatter normalization reveals a small decrease (∼0.1 dB) in HH backscatter after harvest. However, this decrease is too small for monitoring shoot growth. The decrease is not clear at VV polarization. This is attributed to the predominantly horizontal orientation of the harvested leaves
Detection of temporarily flooded vegetation using time series of dual polarised C-band synthetic aperture radar data
The intense research of the last decades in the field of flood monitoring has shown that microwave
sensors provide valuable information about the spatial and temporal flood extent. The new
generation of satellites, such as the Sentinel-1 (S-1) constellation, provide a unique, temporally
high-resolution detection of the earth's surface and its environmental changes. This opens up new
possibilities for accurate and rapid flood monitoring that can support operational applications. Due
to the observation of the earth's surface from space, large-scale flood events and their spatiotemporal changes can be monitored. This requires the adaptation of existing or the development of
new algorithms, which on the one hand enable precise and computationally efficient flood
detection and on the other hand can process a large amounts of data.
In order to capture the entire extent of the flood area, it is essential to detect temporary flooded
vegetation (TFV) areas in addition to the open water areas. The disregard of temporary flooded
vegetation areas can lead to severe underestimation of the extent and volume of the flood. Under
certain system and environmental conditions, Synthetic Aperture Radar (SAR) can be utilized to
extract information from under the vegetation cover. Due to multiple backscattering of the SAR
signal between the water surface and the vegetation, the flooded vegetation areas are mostly
characterized by increased backscatter values. Using this information in combination with a
continuous monitoring of the earth's surface by the S-1 satellites, characteristic time series-based
patterns for temporary flooded vegetation can be identified. This combination of information
provides the foundation for the time series approach presented here.
This work provides a comprehensive overview of the relevant sensor and environmental
parameters and their impact on the SAR signal regarding temporary open water (TOW) and TFV
areas. In addition, existing methods for the derivation of flooded vegetation are reviewed and their
benefits, limitations, methodological trends and potential research needs for this area are identified
and assessed. The focus of the work lies in the development of a SAR and time series-based
approach for the improved extraction of flooded areas by the supplementation of TFV and on the
provision of a precise and rapid method for the detection of the entire flood extent.
The approach developed in this thesis allows for the precise extraction of large-scale flood areas
using dual-polarized C-band time series data and additional information such as topography and
urban areas. The time series features include the characteristic variations (decrease and/or
increase of backscatter values) on the flood date for the flood-related classes compared to the
whole time series. These features are generated individually for each available polarization (VV,
VH) and their ratios (VV/VH, VV-VH, VV+VV). The generation of the time series features was
performed by Z-transform for each image element, taking into account the backscatter values on
the flood date and the mean value and standard deviation of the backscatter values from the nonflood dates. This allowed the comparison of backscatter intensity changes between the image
elements. The time series features constitute the foundation for the hierarchical threshold method
for deriving flood-related classes. Using the Random Forest algorithm, the importance of the time
series data for the individual flood-related classes was analyzed and evaluated. The results showed
that the dual-polarized time series features are particularly relevant for the derivation of TFV.
However, this may differ depending on the vegetation type and other environmental conditions.
The analyses based on S-1 data in Namibia, Greece/Turkey and China during large-scale floods
show the effectiveness of the method presented here in terms of classification accuracy. Theiv
supplementary integration of temporary flooded vegetation areas and the use of additional
information resulted in a significant improvement in the detection of the entire flood extent. It
could be shown that a comparably high classification accuracy (~ 80%) was achieved for the flood
extent in each of study areas. The transferability of the approach due to the application of a single
time series feature regarding the derivation of open water areas could be confirmed for all study
areas. Considering the seasonal component by using time series data, the seasonal variability of the
backscatter signal for vegetation can be detected. This allows for an improved differentiation
between flooded and non-flooded vegetation areas. Simultaneously, changes in the backscatter
signal can be assigned to changes in the environmental conditions, since on the one hand a time
series of the same image element is considered and on the other hand the sensor parameters do
not change due to the same acquisition geometry. Overall, the proposed time series approach
allows for a considerable improvement in the derivation of the entire flood extent by
supplementing the TOW areas with the TFV areas
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 “Valley of the Kings” (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
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
- …