PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
This PDF file contains the front matter associated with SPIE
Proceedings Volume 7829, including the Title Page, Copyright
information, Table of Contents, and the Conference Committee listing.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
Joint Session with Conference 7830: SAR Data Analysis I
Goal of this paper is an evaluation of Bayesian estimators: Minimum Mean Square Error (MMSE), Minimum
Mean Absolute Error (MMAE) and Maximum A-posteriori Probability (MAP). Such estimations have been
carried out in the undecimated wavelet domain. Bayesian estimation requires probability density function (PDF)
models for the wavelet coefficients of the reflectivity and of the signal-dependent noise. In this work several
combination of PDFs will be assessed. Closed-form solutions for MMSE, MMAE and MAP have been derived,
whenever possible; numerical solutions otherwise. Experimental results carried out on simulated noisy images
evidence the cost-performance trade off of the different estimators in conjunction with PDF models. MAP
estimation with generalized Gaussian (GG) PDF for wavelet coefficients of both reflectivity and signal-dependent
noise (GG - GG) yields best performances. MAP with Laplacian - Gaussian (L - G) is only 0.07 dB less performing
than MAP with GG - GG. However, the former admits a closed-form solution and its computational cost is more
than ten times lower than that of the latter. Results on true single look high-resolution Cosmo-SkyMed SAR
images provided by Italian Space Agency (ASI), are presented and discussed.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
Change detection provides a powerful means for the initial detection of small target objects. However, speckle effects
mean this type of approach can be difficult to apply to Synthetic Aperture Radar (SAR) imagery. This paper examines
one method for target detection using change between a registered pair of SAR images. The technique may be
parameterized to detect small target objects ranging in size from a few to perhaps a few hundred pixels. The approach
considered here exploits the observation that the scattering response of many target types of interest is dominated by a
small number of bright scatterers, whilst natural clutter regions tend not to display this property. The variance provides a
useful statistic summarizing this effect, consequently the detection method considered here is based on the ratio of the
variances of corresponding patches in the pair of images. Ideally any test statistic should be characterized by a known
statistical distribution; this will allow formal tests of a null hypothesis to be carried out. Here the null hypothesis
corresponds to no change, and knowledge of the distribution of the test statistic enables the implementation of a Constant
False-Alarm Rate (CFAR) detection process. The analysis carried out herein considers the distribution of the variance
ratio under realistic operating parameterisations for target detection in SAR imagery. Synthetic data is used to
characterize this distribution, and Monte Carlo techniques are applied to derive empirical formulae for use in an online
application. Results are presented for synthetic data and for a registered image pair, in the form of detection maps.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
Joint Session with Conference 7830: SAR Data Analysis II
Synthetic Aperture Radar (SAR) is the only remote sensing technology that can provide high resolution images in
adverse weather conditions and in day or night times. It is thus is a powerful tool for Earth monitoring. Certain
applications, such as disaster relief, military reconnaissance and ice-flow and ship monitoring require a continuous flow
of high-resolution images covering large areas; however, given the large amount of complex data generated and system
limitations of data bandwidth and processing speed, not all the requirements can be met at the same time. In addition,
multiple user requests are often submitted to the SAR system platform, and not all can be addressed, again due to
limitations of area coverage. Increasing the speed of SAR processors and processing on-board are two ways to improve
the SAR data throughput and therefore to meet the operational needs of all users.
This paper discusses an optronic SAR processor capable of rapidly processing full-scene multi-looked images. Details of
the processor design and image results are discussed. Estimations for speed and image throughput are provided, all
presented in the context of the requirements for operational service of the various applications.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
In this paper we investigate an unsupervised neural network approach for automatically extracting objects of interest
from very high resolution (VHR) SAR images. The technique is based on the use of Pulse-Coupled Neural Networks
(PCNN) which is a relatively novel technique based on models of the visual cortex of small mammals. The study
discusses the use of PCNN technique in different applications. In a first case the extraction procedure is focused on the
detection of buildings. In the second case the segmentation of a dark spot representing an oil spill in a SAR image is
considered. The performance yielded by the PCNN is evaluated and critically discussed for a set of new generation of X-band
SAR images taken by COSMO-Skymed and TerraSAR-X systems.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
With the increasing availability of high resolution SAR imagery like RADARSAT-2 and TerraSAR-X, it becomes
interesting to investigate the potential of this type of data for urban applications. There is however a great obstacle in
using SAR imagery of urban areas: the corner reflector or "cardinal effect" problem. It is greatly problematic when
multiple images of the same scene are taken from different azimuth angle. We propose a novel framework to overcome
this problem by using contextual information about road orientation and building position to correct higher than normal
pixel intensity caused by corner reflectors.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
TerraSAR-X is the Synthetic Aperture Radar (SAR) German satellite which provides a high diversity of information
due to its high-resolution. TerraSAR-X acquires daily a volume of up to 100 GB of high complexity,
multi-mode SAR images, i.e. SpotLight, StripMap, and ScanSAR data, with dual or quad-polarization, and with
different look angles. The high and multiple resolutions of the instrument (1m, 3m or 10m) open perspectives
for new applications, that were not possible with past lower resolution sensors (20-30m). Mainly the 1m and
3m modes we expect to support a broad range of new applications related to human activities with relevant
structures and objects at the 1m scale. Thus, among the most interesting scenes are: urban, industrial, and
rural data. In addition, the global coverage and the relatively frequent repeat pass will definitely help to acquire
extremely relevant data sets. To analyze the available TerrrSAR-X data we rely on model based methods for
feature extraction and despeckling.
The image information content is extracted using model-based methods based on Gauss Markov Random
Field (GMRF) and Bayesian inference approach. This approach enhances the local adaptation by using a prior
model, which learns the image structure and enables to estimate the local description of the structures, acting
as primitive feature extraction method. However, the GMRF model-based method uses as input parameters the
Model Order (MO) and the size of Estimation Window (EW). The appropriated selection of these parameters
allows us to improve the classification and indexing results due to the number of well separated classes could be
determined by them. Our belief is that the selection of the MO depends on the kind of information that the
image contains, explaining how well the model can recognize complex structures as objects, and according to the
size of EW the accuracy of the estimation is determined.
In the following, we present an evaluation of the impact of the model order selection and the estimation
windows size using TerraSAR-X data. We determine how many classes can be indexed depending on the Model
Order and Estimation Window. The experimental results shows a good choice is model order 3 and 4, and
estimation window with radius 15 × 15 pixels size.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
The very-high spatial resolution provided by COSMO-Skymed products, also considering the concurrent TerraSAR-X
mission, opens new challenges in the field of SAR image processing for remote sensing applications, maybe comparable
to those represented by the first optical commercial satellites at the beginning of last decade. The Tor Vergata-Frascati
test site, where extensive ground-truth data are available, was imaged by the COSMO constellation at two different days
in summer 2010. This enabled first investigations on the potential of this type of imagery in providing a characterization
of sub-urban areas by exploitation of both amplitude and phase information contained in the radar return. In particular
this paper deals with the set-up of preliminary chains of automatic processing based on Multi-Layer Perceptron neural
networks for pixel based analysis. Also some comments concerning the retrieval of information on the vertical properties
of a single building are reported.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
Soil moisture estimation is one of the most challenging problems in the context of biophysical parameter estimation from
remotely sensed data. Typically, microwave signals are used thanks to their well known sensitivity to variations in the
water content of soil. However, other target properties such as soil roughness and the presence of vegetation affect the
microwave signals, thus increasing the complexity of the estimation problem. The latter problem becomes even more
complex when we move on mountain areas, such as the Alps, where the high heterogeneity of the topographic condition
further affect the signals acquired by remote sensors. In this paper, we explore the use of polarimetric RADARSAT2
SAR images for the estimation of soil moisture content in an alpine catchment. In greater detail, we first exploit field
measurements and ancillary data to carry out an analysis on the sensitivity of the SAR signal to the moisture content of
soil and other target properties, such as topography and vegetation/land-cover heterogeneity, that characterize the
mountain environment. On the basis of the findings emerged from this analysis, we propose a technique for estimating
moisture content of soils in these challenging operative conditions. This technique is based on the Support Vector
Regression algorithm and the integration of ancillary data. Preliminary results are discussed both in terms of accuracy
over point measurements and effectiveness in handling spatially distributed data.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
The support of Earth Observation Systems to flood monitoring and damage assessment has been evaluated by integrating
skills and knowledge of hydrologists and experts on image processing. Detailed flood maps are generated from the
analysis of images pairs acquired by Cosmo-Skymed on the same area at different times. The adopted methodology
applies image pre-processing and segmentation techniques with a multi-temporal approach. After noise reduction by
despeckling, the user manually localizes few water points in one image. From selected points, segmentation distinguishes
the classes of flood, permanent-water and no-change areas. Then, an anisotropic scanning analyzes the images to define
its content. The resulting connected regions are converted into vectors to be entered as constraint for a Physically based
2-D hydraulic flood model. The model recursively varies the unknown boundary conditions to match at best the areas
extracted from Cosmo-Skymed. The product is a hydraulic consistent report of the flooded area including information on
water depth and velocities. By combining these information with vulnerability maps, extracted from optical satellite
images with a supervised approach, an estimation of the damage is provided. The reported results refer to the monitoring
of the flood event occurred in the Scutari Lake area (Albania, January 2010).
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
In general algorithms for soil moisture retrieval from high resolution satellite data cannot be easily extended to areas
where they have not been calibrated and validated. This paper presents the application of an innovative approach for the
detection of soil moisture from high resolution SAR images in order to overcome this main limitation by introducing a
priori information.
During the training phase, extensive data sets of SAR images and related ground truth on four areas characterized by
very different surface features have been analyzed in order to understand the ENVISAT/ASAR responses to different
soil, environmental and seasonal conditions. From preliminary analyses, the comparison of the backscattering
coefficients in dependence of soil moisture values for all the analyzed datasets indicates the same sensitivity to soil
moisture variations but with different biases, which may depend on soil characteristics, vegetation presence and
roughness effect. These bias values have been used to introduce an adaptive term in the electromagnetic formulation of
the backscattering responses from natural bare surfaces. The simulated data from this new model have been then used to
train a neural network to be used then as an inversion algorithm. Preliminary results indicate an improvement in the
accuracy of soil moisture retrieval with respect to the use of a traditional neural network approach. The results have been
also compared with the estimates derived from the application of a Bayesian approach.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
This paper presents the analysis of C and X band images in the scope of soil moisture detection in agricultural fields.
Archived data have been analyzed in order to understand the SAR signal behavior of vegetated fields in comparison to
bare soils. The results indicate that the sensitivity to bare fields of C and X band signatures is very close, while it
changes in presence of vegetation. In particular the effect is directly proportional to amount of vegetation that in this
preliminary analysis has been evaluated through the NDVI variable.
After this analysis, a statistical approach has been applied to SAR images to infer the information on the soil moisture
values. Several experiments have been carried out by considering only C band data, only X band data and a combination
of C and X band data. For bare soils, C and X band data determine very similar results and in good agreement to ground
measurements. For vegetated fields, C band data tend to underestimate soil moisture due to the vegetation attenuation,
while X band data, mainly influenced by vegetation, determine very poor results. Encouraging results are obtained by the
combination of C and X band data, thus indicating that X band data can be used in combination to C band data in order
to compensate the effect of vegetation.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
The PSInSAR technique, invented by Ferretti et. al. [1], [2], [3] ten years ago, meanwhile has proven its capability for
very precise measurement of surface deformations. To achieve this, the influence of the atmospheric phase screen (APS)
has to be removed. We investigated the APS for two series of TerraSAR-X high resolution spotlight data of a scene in
Bavaria. In order to account for stratified troposphere and turbulence we augmented the APS estimation of StaMPS
(Stanford Method for Persistent Scatterers) [4], that is we consider the APS as composed of a phase ramp, a part
stratified with height and a turbulent component. The turbulent component is estimated via kriging. The necessary
variograms can be computed under the assumption of isotropy as well as allowing for anisotropy. For short distances the
variograms show a regime which is not visible for lower resolutions. In this paper we discuss the choice of appropriate
variogram models with respect to our data.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
The recent availability of wide-bandwidth, high-frequency, high-resolution SAR data is contributing to improved monitoring
capabilities of spaceborne remote sensing instruments. In particular, the new COSMO/SkyMed (CSK) and TerraSAR-
X (TSX) X-band sensors allow better performances in multitemporal DInSAR and PSI applications than legacy
C-band sensors such as ENVISAT ASAR, with respect to both target detection and terrain displacement monitoring
capabilities. In this paper we investigate about the possibility of achieving performances of PSI displacement detection
comparable to those of C-band sensors, by use of reduced numbers of high-resolution X-band acquisitions. To this end,
we develop a simple model for phase and displacement rate measurement accuracies taking into account both target
characteristics and sensors acquisition schedule. The model predicts that the generally better resolution and repeat-time
characteristics of new-generation X-band sensors allow reaching accuracies comparable to C-band data with a significantly
smaller number of X-band acquisitions, provided that the total time span of the acquisitions is large enough. This
allows in principle to contain the costs of monitoring campaigns, by using less scenes. Indications are more variable in
the case of short-time acquisition schedules, such as those involved in the generation of so-called "rush products" for
emergency applications. In this case, the higher uncertainty given by shorter total time spans lowers X-band performances
to levels mostly comparable to those of the legacy medium-resolution C-band sensors, so that no significant
gain in image number budget are foreseen. These theoretical results are confirmed by comparison of three PSI datasets,
acquired by ENVISAT ASAR, CSK and TSX sensors over Assisi (central Italy) and Venice.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
Satellite SAR Interferometry (InSAR) has been already proven to be effective in the analysis of seismic events. In fact,
the surface displacement field obtained by InSAR application contains useful information to define the fault geometry
(such as dip and strike angles, width, length), the extension of the rupture, the distribution of slip on the fault plain.
However, the solution of the inverse problem, which means to recover the source parameters from the knowledge of
InSAR surface displacement field, is rather complex. In this work we propose an inversion approach for the seismic
source classification and the fault parameter quantitative retrieval based on neural networks. The network is trained by
using a simulated data set generated by means of a forward model. The application of the methodology has been
validated with a set of experimental data corresponding to different types of seismic events.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
Spectral clustering has become one of the most popular modern clustering algorithms in recent years. In this paper, a new
algorithm named entropy ranking based adaptive semi-supervised spectral clustering for SAR image segmentation is
proposed. We focus not only on finding a suitable scaling parameter but also determining automatically the cluster
number with the entropy ranking theory. Also, two kinds of constrains must-link and cannot-link based semi-supervised
spectral clustering is applied to gain better segmentation results. Experimental results on SAR images show that the
proposed method outperforms other spectral clustering algorithms.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
Two kinds of doubly peaked ocean wave spectra such as Torsethaugen spectrum and Ochi-Hubble spectrum are used to
simulate the mixed ocean waves with both swells and wind seas. The Envisat ASAR (Advanced Synthetic Aperture
radar) image cross spectra of mixed ocean waves in different significant wave height (SWH), wave direction, wave
component and peak period are then simulated by using Engen's nonlinear transformation formula. Analysis based on the
simulation indicate that (1) in addition to the contribution of wind wave part and swell part of the mixed waves, the cross
spectra of mixed waves consist of an extra term; (2) the cross spectra of mixed ocean waves dilate in range direction and
shrink in azimuth direction (the so-called azimuth cutoff effect) and the cutoff effect increases for waves with larger
wave height, or for waves propagating closer the azimuth direction, or for waves containing more wind wave component,
or for waves with shorter peak period; (3) the cross spectra split into two parts for waves propagating along range
direction; (4) the direction ambiguity of ocean waves can be removed by using the imaginary part of cross spectra.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.