Many approaches for land cover classification rely on the spectral characteristics of the elements on the surface using one single multispectral image. Some land cover elements, as the vegetation and, in particular crops, are changing over seasons and over the growing cycle and may be characterized by their spectral temporal variability. In such cases, the spectral temporal variability can be used to model the crop’s phenology and predict the crop type using both spatial and temporal spectral data. In this paper we aim to exploit the temporal dimension on the crop type classification using multi-temporal multispectral data and machine learning techniques. The high revisiting frequency of Sentinel-2 satellite opens new possibilities on the exploitation of high temporal resolution multispectral data. In this investigation, we evaluated the K-nearest neighbor (KNN), Random Forest (RF) and Decision Tree (DT) methods, for mapping 18 summer crops using Sentinel-2 data. Each method was applied to three different combinations of bands: a) all Sentinel-2 spectral bands (except band 10); b) vegetation indices (NDVI, EVI), Water Indices (NDWI, NDWI2, Moisture Index) and Normalized Image Indices and Brightness and c) the combination of the spectral bands and the indices. The best precisions we achieved were 98,6% with KNN, 98,9% with RF and 98.0% using a DT.
This paper presents the results of a study aiming to identify targets in Synthetic Aperture Radar (SAR) images having different properties in terms of microwave scattering and temporal stability using the interferometric SAR coherence and the Principal Component Analysis (PCA). Coherence maps used in this analysis are generated starting from a time series of Sentinel-1 images. A flat area in Padan plain (Italy), characterized by agricultural fields with different crops, urban settlements and water surfaces is chosen as study area.
This paper presents the results of an experiment aiming to measure the vibrational frequencies of the main structures of the medieval church of San Domenico (Matera, southern Italy) and relate them to the mechanical properties of geological stratigraphy and construction materials. Vibrational frequencies are measured by means of the ground-based radar inteferometry technique using a Ku-band radar. Time series of ground-based radar data are processed to measure displacements and vibration frequencies of the church structures. Data collected by a seismic triaxal station for the measurement of the H/V ratio are also processed to compare with radar-based frequencies measurements, providing information about the main vibration frequency ranges of the church structures and, more important, disentangle the site and structure vibration frequencies.
In this work we study the problem of mapping soil moisture by means of Synthetic Aperture Radar (SAR) images. A test site has been set in Companhia das Lezirias, close to Lisbon, Portugal. The main advantage of using SAR images is their capability to map soil moisture at a very high spatial resolution. This opens interesting perspectives for agricultural applications, where soil moisture can abruptly change across field boundaries depending on the agricultural practices. The study area is characterized by flat topography, large agricultural areas and sparse vegetation. Five sensors have been deployed in a test area to measure soil moisture with a sampling time of one hour for a period of seven months. In-situ measurements are compared with the results obtained by processing 33 C-band Sentinel-1 images using the SAR interferometry technique. The aim of the study is to analyze the relation between the interferometric phase and time varying soil moisture. The main advantage of SAR interferometry with respect to the use of radar cross-section is that the information about soil moisture can be recovered using a reduced number of in-situ measurements. In particular, we combine three interferograms obtained from three SAR images, acquired over the same area at different times, to derive maps of bi-coherence and phase triplet. This last quantity allows to disentangle the phase contribution due to soil moisture from those related to microwave propagation in atmosphere and terrain displacements. Results are compared to those obtained using the interferometric phase and coherence to emphasize the importance to split the effects due to propagation (e.g. atmosphere) from those related to volume scattering.
In this study, an experiment aimed to integrate Global Navigation Satellite System (GNSS) atmospheric data with meteorological data into a neural network system is performed. Precipitable Water Vapor (PWV) estimates derived from GNSS are combined with surface pressure, surface temperature and relative humidity obtained continuously from ground-based meteorological stations. The work aims to develop a methodology to forecast short-term intense rainfall. Hence, all the data is sampled at one hour interval. A continuous time series of 3 years of GNSS data from one station in Lisbon, Portugal, is processed. Meteorological data from a nearby meteorological station are collected. Remote sensing
data of cloud top from SEVIRI is used, providing collocated data also on an hourly basis. A 3 year time series of hourly accumulated precipitation data are also available for evaluation of the neural network results. In previous studies, it was found that time varying PWV is correlated with rainfall, with a strong increase of PWV peaking just before intense rainfall, and with a strong decrease afterwards. However, a significant amount of false positives was found, meaning that the evolution of PWV does not contain enough information to infer future rain. In this work a multilayer fitting network is used to process the GNSS and meteorological data inputs in order to estimate the target outputs, given by the hourly
precipitation. It is found that the combination of GNSS data and meteorological variables processed by neural network improves the detection of heavy rainfall events and reduces the number of false positives.
Observing the water vapor distribution on the troposphere remains a challenge for the weather forecast. Radiosondes provide precise water vapor profiles of the troposphere, but lack geographical and temporal coverage, while satellite meteorological maps have good spatial resolution but even poorer temporal resolution. GPS has proved its capacity to measure the integrated water vapor in all weather conditions with high temporal sampling frequency. However these measurements lack a vertical water vapor discretization. Reconstruction of the slant path GPS observation to the satellite allows oblique water vapor measurements. Implementation of a 3D grid of voxels along the troposphere over an area where GPS stations are available enables the observation ray tracing. A relation between the water vapor density and the distanced traveled inside the voxels is established, defining GPS tomography. An inverse problem formulation is needed to obtain a water vapor solution. The combination of precipitable water vapor (PWV) maps obtained from MODIS satellite data with the GPS tomography is performed in this work. The MODIS PWV maps can have 1 or 5 km pixel resolution, being obtained 2 times per day in the same location at most. The inclusion of MODIS PWV maps provides an enhanced horizontal resolution for the tomographic solution and benefits the stability of the inversion problem. A 3D tomographic grid was adjusted over a regional area covering Lisbon, Portugal, where a GNSS network of 9 receivers is available. Radiosonde measurements in the area are used to evaluate the 3D water vapor tomography maps.
The need of reliable monitoring of old embankment dams is rapidly increasing since a large number of these structures
are still equipped with old monitoring devices, usually installed some decades ago, which are generally capable to
provide only localized information on specific areas of the embankment. This work discusses the use of Ground-Based
Synthetic Aperture Radar (GBSAR) interferometry technique to observe and control the structural behavior of earthfill
or rockfill embankments for dam impoundments. This non-invasive technique provides displacements patterns measured
with sub-millimeter precision. Monitoring strategies of earthfill dam embankment in Southern Italy are presented.
A Ground-Based Synthetic Aperture Radar (GB-SAR) is nowadays employed in several applications. The processing of
ground-based, space and airborne SAR data relies on the same physical principles. Nevertheless specific algorithms for
the focusing of data acquired by GB-SAR system have been proposed in literature.
In this work the impact of the main focusing methods on the interferometric phase dispersion and on the coherence has
been studied by employing a real dataset obtained by carrying out an experiment. Several acquisitions of a scene with a
corner reflector mounted on a micrometric screw have been made; before some acquisitions the micrometric screw has
been displaced of few millimetres in the Line-of-Sight direction. The images have been first focused by using two
different algorithms and correspondently, two different sets of interferograms have been generated. The mean and
standard deviation of the phase values in correspondence of the corner reflector have been compared to those obtained by
knowing the real displacement of the micrometric screw. The mean phase and its dispersion and the coherence values for
each focusing algorithm have been quantified and both the precision and the accuracy of the interferometic phase
measurements obtained by using the two different focusing methods have been assessed.
The problems of simulation of bistatic SAR raw data and focusing are studied. A discrete target simulator is described.
The simulator introduces the scene topography and compute the integration time of general bistatic configurations
providing a means to derived maps of the range and azimuth spatial resolutions. The problem of focusing of bistatic SAR
data acquired in a translational-invariant bistatic configuration is studied by deriving the bistatic Point Target Reference
spectrum and presenting an analytical solution for its stationary points.
Atmosphere water vapour remains the largest limitation in high precision applications that make use of microwave
signals as Interferometric Synthetic Aperture Radar (InSAR). In the last decade several methods like GPS
(Global Positioning System), MERIS (MEdium Resolution Imaging Spectrometer) and NWP (Numerical weather
prediction) models were studied with the aim of obtaining a reliable water vapour product of high spatial and
temporal resolution to reduce the impact that the water vapour have on microwave signals. Water vapour
product derived from the optical sensor MERIS may be used to mitigate the troposphere effects in applications
like InSAR and used to improve NWP models. In this paper the water vapour derived from MERIS and GPS
are compared, and a methodology to combine GPS and MERIS is present.
In this work we present the results of an experiment aiming to measure and model atmospheric delay by means of GPS,
Weather Research and Forecasting (WRF) model and Synthetic Aperture Radar Interferometry (InSAR). Examples of maps
of the atmospheric delay over the region of Lisbon are shown.
A methodology is presented to derive a time series of Precipitable Water Vapour (PWV) maps from interferometric
Synthetic Aperture Radar (SAR) data. Generally, information on PWV spatial distribution provided
by SAR interferomeetry (InSAR), even if characterized by a high spatial resolution, is updated with a temporal
frequency depending the satellite revisiting cycle of the InSAR sensor. The methodology is based on the use of
independent GPS measurements of PWV to set the unknown bias resulting from the unwrapping process in each
unwrapped interferometric phase. The main advantage of this methodology is that calibrated PWV maps can
be derived from each stack of SAR interferograms. This allows the merging of PWV time series obtained over
the same area by different SAR sensors and along different satellite orbits. As a result, the updating frequency
is reduced from the satellite revisitng cycle to a few days.
An algorithm for waterline extraction from SAR images is presented based on the estimation of the geodesic path,
or minimal path (MP) between two pixels on the waterline. For two given pixels, geodesic time is determined
in terms of the time shortest path, between them. The MP is determined by estimating the mean value for
all pairs of neighbor pixels that can be part of a possible path connecting the initial given pixels. A MP is
computed as the sum of those two geodesic image functions. In general, a MP is obtained with the knowledge
of two end pixels. Based on the 2-dimensional spreading of the estimated geodesic time function, the concepts
of propagation energy and strong pixels are introduced and tested for the waterline extraction by marking only
one pixel in the image.
In last decades many interferometric Synthetic Aperture Radar (SAR) applications have been developed aiming
at measuring terrain morphology or deformations. The geodesic information is carried by the interferometric
phase. However, this can be observed only in the principal interval giving the so called wrapped phase. To extract
the interesting information, e.g., height surface or terrain deformation, the absolute phase should be estimated
from the wrapped phase observations. In this work we present an approach to solve PU relying on the local
analysis of the wrapped phase signal gradients to recover fringes and phase jumps. The proposed fringe detection
algorithm is tested on both synthetic and real data. Synthetic phase surfaces are generated characterized by
different signal-to-noise ratio and using a real topographic scene. Real interferograms obtained by processing
ENVISAT and TerraSAR-X SAR images are also used to test the above algorithm. First results show that the
proposed approach is able to recognize and reconstruct fringes in noisy interferograms.
This work presents a morphological-based segmentation approach for coastline detection based on a waterfall
hierarchical scheme. Hierarchical waterfall is constrained my markers in each step on the hierarchical tree. In
this manner, a map of the waterfall minimum persistency is created to identify the coastline. The proposed
algorithm was tested on Envisat-ASAR and TerraSAR-X images acquired over the Lisbon region.
Space missions involve the download,from space to ground, of many raw images per day. The analysis and sharing of these huge amounts of data is a big challenge for the remote sensing community. The emerging computational grid technologies are expected to make feasible the creation of a computational environment handling many PetaBytes of distributed data, tens of thousands of heterogeneous computing resources, and thousands of simultaneous users from multiple research institutions. The first results obtained in an experiment aiming to demonstrate the use of grid technology for remote sensing applications will be shown. The experiment has been carried out within the DataGrid project funded by the European Union.
Ground-based Synthetic Aperture Radar (GB-SAR) interferometry is used to monitor the Tessina landslide. This is a complex mass movement in the Italian Eastern Alps. Radar data, acquired during a 10-day campaign, are interferometrically processed. Image couples taken at the same position but at different times are used to estimate terrain slope deformations on a short temporal scale of a few hours. In addition, SAR images acquired with a baseline are processed to extract information on the current topography of the landslide area. Comparison with the terrain morphology of the scene at earlier times allows to evaluate the landslide activity on a long time scale spanning a few years.
KEYWORDS: Interferometry, Synthetic aperture radar, Radar, Antennas, Nanoimprint lithography, Landslides, Ku band, Data acquisition, Interferometers, L band
A ground-based Synthetic Aperture Radar (GB-SAR) interferometric technique is proposed for the topographic mapping. It is based on a coherent continuous-wave step-frequency radar moved along a horizontal rail to generate the synthetic aperture. Antennas are placed on a mechanical arm whose rotation enables to create an interferometric baseline. The synthetic aperture is 3 m long and the average radar-to-scene distance is 1200 m. Multifrequency measurements are carried out at Schwaz, Austria, using different baselines. The interferometrically derived topography is compared with an existing Digital Elevation Model (DEM) of the area.
Phase unwrapping (PU), i.e. the retrieval of absolute phases from wrapped, noisy phase measurements, is a tough problem which arises in various realms of signal processing. Generally, the adopted PU strategy consists in integrating the estimated phase gradients or instantaneous frequencies (IFs). The standard IF estimation technique used by most PU algorithms is the simple wrapping-of-the-wrapped-phase-gradient rule. However, this rule results in a strongly biased estimations as the noise level grows. The idea on which this paper relies consists in locally approximating the interferogram by a sum of complex exponentials. The algorithm described in this paper makes use of the Matrix Pencil (MP) method for estimating the parameters of these exponentials, i.e. their amplitude and frequency. The proposed algorithm is applied to the unwrapping of noisy synthetic interferograms. The results are compared with those obtained unwrapping the same interferograms by means of the classical wrapping-of-the-wrapped-phase-gradient rule.
Three measurements are required to reconstruct the topographic information by means of Synthetic Aperture Radar Interferometry (InSAR): range, azimuth, and elevation. The first is obtained by timing the return of the radar pulse, the second by observing its Doppler frequency shift, and the third by measuring the phase difference between signals recovered at the spatially displaced antennas. In this paper a new general scheme for the geolocation of InSAR information is presented. It avoids the use of an Earth model and exploits the full information of a SAR interferometer: orbit data, range and Doppler frequency shift of each SAR image, and interferometric phase. Two geolocation algorithms are obtained within this scheme. The former studies the geolocation as the intersection of five surfaces defined by measurements of range, Doppler frequency shift and interferometric phase. In particular, the five surfaces are: the range spheres centered at the SAR antennas which is also where the Doppler frequency shift cones are located. The interferometric phase adds another surface - a phase hyperboloid - whose axis of symmetry is the interferometer baseline. These five surfaces intersect at two locations in space. One of them is the geolocation of image pixel. The last geolocation algorithm is based on the solution of a set of four equation: the range spheres and the Doppler frequency shift cones of the two SAR images. An exact closed-form solution is obtained. This solution does not rely on approximations and avoids the use of iterative algorithms. This results in a reduction of the computational load. Moreover, the proposed algorithms give a scheme for computing the geolocation accuracy.
In the last years, both local and global analysis techniques for the effective processing of interferometric SAR data have been proposed. We developed two local approaches to eliminate inconsistencies in the measured (wrapped) phase field, based on the local configurations of phase gradients in finite windows. The first technique adopts a fixed search strategy which 'cures' isolated residue couples by an appropriate series of corrections determined a priori. A second strategy uses the generalization capabilities of a neural network, trained on a suitable number of simulated target phase fields, to add 2 - (pi) cycles to the proper locations of the interferogram. These approaches, in spite of the high dimensionality of this problem, are able to correctly remove more than half the original number of pointlike inconsistencies on real noisy interferograms. This stems from the observation that phase unwrapping is an ill-posed problem, which has to be solved globally. Hence, a global stochastic method has been implemented, based on the minimization of a functional measuring the regularity of the phase field. The optimization tool used is simulated annealing with constraints. This methodology gives excellent results also in difficult conditions. We will present some of the recent results which aim at integrating the above-mentioned methodologies into powerful processing chains optimized for operating on large IFSAR datasets from real scenes. The effectiveness of such phase retrieving methods allows the application of sophisticated and innovative remote sensing techniques, such as differential interferometry.
SAR interferometry can be used to derive topographic information (DEM) on the earth surface. An operation called geocoding is necessary to translate the DEM form a range- azimuth to a latitude-longitude reference system. This paper introduces a new algorithm for geocoding interferometric DEMs based on an iterative procedure using the reference ellipsoid as the first guess, then locating each earth point on a succession of planes locally parallel to the ellipsoid. The procedure is shown to geometrically converge to the considered DEM point. Given its high accuracy, this method could be used as a precision tool for use in difficult zones.
We propose a new method for phase unwrapping based on Stochastic Relaxation. A cost functional is introduced as a measure of the smoothness of the unwrapped phase field. Hence the problem of reconstructing the unwrapped phase field can be formulated as a minimization problem for the integer deviations between the measured and the unknown neighboring pixel differences of the true phase, with the constraint that the deviations have to remove the inconsistencies of the measured phase differences. The optimization problem is solved by the simulated annealing with constraint technique. We tested our method on simulated and real interferograms in presence of aliasing and noise. We remark that our method does not remove noise: in the case of noisy phase fields, our method's output is very near to the input surface noise included. The consistency and efficiency of our method are also demonstrated by comparative test using least squares methods.
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