Sapienza Università di Roma

The assessment of satellite image classifications is usually carried out using a test sample assumed as the ground truth, from which a confusion matrix is derived. There are cases where the reference data, even those coming from a ground survey, are affected by errors and do not represent a reliable truth. In the field of geophysical parameter retrieval, the triple collocation (TC) technique is applied for validating remotely sensed products when the source of test data (e.g., ground data) does not represent a reliable reference. TC is able to retrieve the error variances of three systems observing the same target parameter, assuming that their errors are independent. In this paper, we exploit the same idea to test the classification accuracy in cases where the ground truth is not available. We extend the TC approach to the classification problem for a general number of classes, but we solve it numerically for a two-class problem (i.e., collapsed and noncollapsed buildings). The specific case refers to the detection of L'Aquila 2009 earthquake damage from very high-resolution optical data. The image classification, performed by exploiting an object-based analysis, is compared with those from two different ground surveys carried out after the earthquake by different teams and with different purposes. This paper demonstrates the power of the TC approach for assessing the classification accuracy with no reliable ground truth available, and provides an insight into the problem of assessing damage, from satellite and on ground, in a very critical and unsafe situation, like the one occurring after an earthquake. Moreover, it was found that the remotely sensed product can have an order of accuracy comparable to that of at least one of the ground surveys.

Two Cosmo-SkyMed observations of the inundation occurred in Myanmar on May 2008 are analyzed in this paper to study the potentiality of this sensor for flood mapping . The first image is temporally close to the peak of the event, while the other one was acquired one week later. Our study accounts for the physics of the radar return in the presence of water surfaces. In particular, both specular reflection, typical of open water, and double bounce backscattering, typical of forested and urban areas, are considered. From the analysis of the Cosmo-SkyMed images, a map representing the extension of the flood at the time of the first radar acquisition is derived.

In case of a seismic event, a fast and draft damage map of the hit urban areas can be very useful, in particular when the epicentre of the earthquake is located in remote regions, or the main communication systems are damaged. Our aim is to analyse the capability of remote sensing techniques for damage detection in urban areas and to explore the combined use of radar (SAR) and optical satellite data. Two case studies have been proposed: Izmit (1999; Turkey) and Bam (2003; Iran). Both areas have been affected by strong earthquakes causing heavy and extended damage in the urban settlements close to the epicentre. Different procedures for damage assessment have been successfully tested, either to perform a pixel by pixel classification or to assess damage within homogeneous extended areas. We have compared change detection capabilities of different features extracted from optical and radar data, and analysed the potential of combining measurements at different frequency ranges. Regarding the Izmit case, SAR features alone have reached 70% of correct classification of damaged areas and 5 m panchromatic optical images have given 82%; the fusion of SAR and optical data raised up to 89% of correct pixel‐to‐pixel classification. The same procedures applied to the Bam test case achieved about 61% of correct classification from SAR alone, 70% from optical data, while data fusion reached 76%. The results of the correlation between satellite remote sensing and ground surveys data have been presented by comparing remotely change detection features averaged within homogeneous blocks of buildings with ground survey data.

Earth Observation (EO) data are used to map mostly affected urban areas after an earthquake generally exploiting change detection techniques applied at pixel scale. However, Civil Protection Services require damage assessment of each building according to a well-established scale to manage rescue operations and to estimate the economic losses. Considering the earthquake that hit L'Aquila city (Italy) on April 6, 2009, this work assess the feasibility of producing damage maps at the scale of single building from Very High Resolution (VHR) optical images collected before and after the seismic event. We considered the European Macroseismic Scale 1998 (EMS-98) and assessed the possibility to discriminate between collapsed or heavy damaged buildings (damage grade DG equal to 5 in the EMS-98 scale) and less damaged or undamaged buildings (DG < 5 in the EMS-98). The proposed approach relies on a pre-existing urban map to identify image objects corresponding to building footprints. The image analysis is carried out according to many different parameters with the objective of assessing their effectiveness in singling out changes associated to the building collapse. Features describing texture and colour changes, as well statistical similarity and correlation descriptors, such as the Kullbach Leibler Distance and the Mutual Information, were included in our analysis. Two supervised classification approaches, respectively, based on the use of the Bayesian Maximum A Posteriori (MAP) criterion and on Support Vector Machines (SVM), were compared. In our experiment, we considered the whole L'Aquila historical centre comparing classification results with the ground survey performed by the Istituto Nazionale di Geofisica e Vulcanologia (INGV). The work represents one of the first attempt to detect damage at the scale of single building, validated against an extensive ground survey. It addresses methodological aspects, highlighting the potential of textural features computed at object scale and SVMs, and discuss potential and limitations of EO in this field compared to ground surveys.

Detection and location of buried structures using the electromagnetic impulsive methodologies (GPR) require the study of the spatial distribution of energy irradiated by an antenna into the ground and the mechanisms of wave propagation and scattering from relevant targets. Evaluation of the difference in wave field distribution in the ground with respect to free space can provide some useful indications on the propagation of the Geo-radar signal in the ground and the spatial resolution capability of the GPR method. For this reason, a research group, involving “La Sapienza” University, Rome and the National Research Council began, during 1992, to perform studies on antenna radiation pattern, the propagation and scattering phenomena of GPR. This paper presents the experimental set up and the obtained results on the antenna radiation pattern.

We consider statistical-mechanics models for spin systems built on hierarchical structures, which provide a simple example of non-mean-field framework. We show that the coupling decay with spin distance can give rise to peculiar features and phase diagrams much richer than their mean-field counterpart. In particular, we consider the Dyson model, mimicking ferromagnetism in lattices, and we prove the existence of a number of metastabilities, beyond the ordered state, which become stable in the thermodynamic limit. Such a feature is retained when the hierarchical structure is coupled with the Hebb rule for learning, hence mimicking the modular architecture of neurons, and gives rise to an associative network able to perform single pattern retrieval as well as multiple-pattern retrieval, depending crucially on the external stimuli and on the rate of interaction decay with distance; however, those emergent multitasking features reduce the network capacity with respect to the mean-field counterpart. The analysis is accomplished through statistical mechanics, Markov chain theory, signal-to-noise ratio technique, and numerical simulations in full consistency. Our results shed light on the biological complexity shown by real networks, and suggest future directions for understanding more realistic models.

In this paper we study Markov processes and related first-passage problems on a class of weighted, modular graphs which generalize the Dyson hierarchical model. In these networks, the coupling strength between two nodes depends on their distance and is modulated by a parameter σ. We find that, in the thermodynamic limit, ergodicity is lost and the "distant" nodes cannot be reached. Moreover, for finite-sized systems, there exists a threshold value for σ such that, when σ is relatively large, the inhomogeneity of the coupling pattern prevails and "distant" nodes are hardly reached. The same analysis is carried on also for generic hierarchical graphs, where interactions are meant to involve p-plets (p>2) of nodes, finding that ergodicity is still broken in the thermodynamic limit, but no threshold value for σ is evidenced, ultimately due to a slow growth of the network diameter with the size.

The retrieval of land surface emissivity from microwave radiometric measurements is useful for monitoring the surface properties without being affected by the contribution of the atmosphere, which can be significant at higher frequencies. It is based on the inversion of the radiative transfer equation, assuming the absence of scattering phenomena. In this work, a method to improve the accuracy of the emissivity estimates through the removal of the effects of the atmosphere from the radiometric data and through the consideration of the surface elevation information is proposed. We have used the Special Sensor Microwave/Imager (SSM/I) observations over Italy throughout 1995. The atmospheric parameters have been derived from the NCEP vertical profiles, whilst the presence of clouds has been detected through METEOSAT images co-located with the SSM/I ones. The data provided by a digital elevation model (DEM) have been also exploited. Monthly average maps of microwave emissivity relative to a geographical area including Italy have been produced to assess the whole estimation procedure, as well as to give examples of monitoring the seasonal trend of this parameter in a mountainous zone (Alps) and in a flat area (Po Plain).

This letter is focused on the microwave signature characterization of the Greenland ice sheet. Such characterization is carried out by exploiting the S- and Ku-band brightness temperatures measured by the radar altimeter RA-2 when it operates as a radiometer during the ENVISAT Commissioning Phase for the purpose of calibrating the receiver. Despite the poor radiometric resolution and the calibration issues, this activity represented a unique opportunity to gather brightness temperatures at frequencies that are not available from current spaceborne microwave radiometers. The analysis of the passive RA-2 data investigates the influence of terrain height and of the temperature of the snow layers on the brightness temperatures at RA-2 bands. The effect of the different penetration depths of the electromagnetic radiation at S- and Ku-bands is also pointed out. Measurements from the Advanced Microwave Scanning Radiometer for the Earth Observing System are used to complement the data provided by RA-2 and to verify their reliability

In this work we study a Hebbian neural network, where neurons are arranged according to a hierarchical architecture such that their couplings scale with their reciprocal distance. As a full statistical mechanics solution is not yet available, after a streamlined introduction to the state of the art via that route, the problem is consistently approached through signal-to-noise technique and extensive numerical simulations. Focusing on the low-storage regime, where the amount of stored patterns grows at most logarithmical with the system size, we prove that these non-mean-field Hopfield-like networks display a richer phase diagram than their classical counterparts. In particular, these networks are able to perform serial processing (i.e. retrieve one pattern at a time through a complete rearrangement of the whole ensemble of neurons) as well as parallel processing (i.e. retrieve several patterns simultaneously, delegating the management of different patterns to diverse communities that build network). The tune between the two regimes is given by the rate of the coupling decay and by the level of noise affecting the system. The price to pay for those remarkable capabilities lies in a network's capacity smaller than the mean field counterpart, thus yielding a new budget principle: the wider the multitasking capabilities, the lower the network load and vice versa. This may have important implications in our understanding of biological complexity.