87 research outputs found
Neural-network approach to multispectral and hyperspectral data analysis for volcanic monitoring
No full textThe Artificial Neural Network (ANN) approach has demonstrated its effectiveness in Geophysics, considered as an universal approximator, being able to model physical nonlinear phenomena and to solve complex inversion problems in a very short time. Indeed once the training phase is completed,
they can be applied in a very fast manner to new data, so that the computational burden required for the data processing is drastically reduced. This characteristic assumes an important role when considering the possible application to high revisit time sensors like Meteosat Second Generation (MSG) SEVIRI. Indeed the algorithms based on radiative transfer model simulations are generally time consuming making its application difficult in near real time.*
*This dissertation continues a line of research started at the Earth Observation Laboratory of the Tor Vergata University in Rome for inverse modelling of volcanic ash mass retrieval from MODIS using ANNs reporting on the latest advances obtained in the development of neural network algorithms for volcanic ash parameters, such as ash particle size and Aerosol Optical Depth (AOD) and SO2 retrieval from MODIS data and extending the research activity including the first attempt of applying ANNs to hyperspectral remote sensed data, emulating an inverse model for simultaneous estimates of SO2 total columnar content and the cloud height. Attention has been paid also on an ANN pruning analysis in order to find significant spectral wavelength in multispectral data for parameter
inversion
A multisensor approach for the 2016 Amatrice earthquake damage assessment
No full textThis work proposes methodologies aimed at evaluating the damage occurred in the Amatrice town by using optical and Synthetic Aperture Radar (SAR) change features obtained from satellite images. The objective is to achieve a damage map employing the satellite change features in a classifier algorithm, namely the Features Stepwise Thresholding (FST) method. The main novelties of the proposed analysis concern the estimation of derived features at object scale and the exploitation of the unsupervised FST algorithm. A segmentation of the study area into several buildings blocks has been done by considering a set of polygons, over the Amatrice town, extracted from the open source Open Street Map (OSM) geo-database.
The available satellite dataset is composed of several optical and SAR images, collected before and after the seismic event.
Regarding the optical data, we selected the Normalised Difference Index (NDI), and two quantities com-ing from the Information Theory, namely the Kullback-Libler Divergence (KLD) and the Mutual Information (MI). In addition, for the SAR data we picked out the Intensity Correlation Difference (ICD) and the KLD parameter. The exploitation of these features in the FST algorithm permits to obtain a plausible damage map that is able to indicate the most affected areas
Volcanic hot spot detection from optical multispectral remote sensing data using artificial neural networks
No full textThis paper describes an application of artificial neural networks for the recognition of volcanic
lava flow hot spots using remote sensing data. Satellite remote sensing is a very effective
and safe way to monitor volcanic eruptions in order to safeguard the environment and the
people affected by such natural hazards. Neural networks are an effective and well-established
technique for the classification of satellite images. In addition, once well trained, they prove
to be very fast in the application stage.
In our study a back propagation neural network was used for the recognition of thermal
anomalies affecting hot lava pixels. The network was trained using the three thermal channels
of the Advanced Very High Resolution Radiometer (AVHRR) sensor as inputs and the corre-
sponding values of heat flux, estimated using a two thermal component model, as reference
outputs.
As a case study the volcano Etna (Eastern Sicily, Italy) was chosen, and in particular the
effusive eruption which took place during the month of 2006 July. The neural network was
trained with a time-series of 15 images (12 nighttime images and 3 daytime images) and
validated on three independent data sets of AVHRR images of the same eruption and on two
relative to an eruption occurred the following month.
While for both nighttime and daytime validation images the neural network identified the
image pixels affected by hot lava with a 100 per cent success rate, for the daytime images also
adjacent pixels were included, apparently not interested by lava flow. Despite these performance
differences under different illumination conditions, the proposed method can be considered
effective both in terms of classification accuracy and generalization capability. In particular
our approach proved to be robust in the rejection of false positives, often corresponding
to noisy or cloudy pixels, whose presence in multispectral images can often undermine the
performance of traditional classification algorithms. Future work shall address application of
the proposed method to data acquired with a high temporal resolution, such as those provided
by the spinning enhanced visible and infrared imager sensor on board the Meteosat second
generation geostationary satellite.Published1525-15355V. Sorveglianza vulcanica ed emergenzeJCR Journalrestricte
Dati iperspettrali nel VIS-SWIR per l’analisi dell’emissione relativa alla banda del Potassio per lo studio di incendi
No full textGli incendi sono un fenomeno che colpisce ogni anno il nostro pianeta ed in particolare anche l’Italia. Oltre all’effetto immediato sul territorio, è stato riconosciuto un effetto a livello di impatto climatico. I fuochi cambiano lo stato fisico della vegetazione rilasciando nell’atmosfera gas che giocano un ruolo importante nell’effetto serra. E’ stato stimato che la biomassa bruciata in un anno, contribuisce al 38% dell’Ozono in troposfera, al 32% di monossido di Carbonio, al 20% degli altri gas (Levine, 1991; Andreae, 1991; Kaufman et al., 1998a,b).
L’uso dei canali termici (8-14 micron) o relativi al vicino infrarosso (1.0 -2.5 micron) sono tradizionalmente utilizzati per la detection e lo studio di parametri fisici come il potere radiante, l’NDBR (Normalised Difference Burn Ratio), o il tasso di combustione della biomassa. Nel visibile una banda di emissione diagnostica dello stato di fiamma, è quella del Potassio (K-method) che fino ad ora non è stata molto studiata in quanto limitata dalle prestazioni degli strumenti. Nell’estate 2006, con il progetto AIRFIRE finanziato da ESA, una campagna aerea è stata effettuata su incendi non controllati utilizzanodo un prototipo di sensore iperspettrale denominato SIM-GA di Selex Galileo. Il SIM-GA è un sensore iperspettrale ad altissima risoluzione spettrale 1.2nm e 2.5 nm rispettivamente nel VISIBILE (350-1200nm) e nel vicino infrarosso(1200-2500nm) che opera in modalità pushbroom. I dati ottenuti hanno permesso di verificare e testare il metodo della panda del Potassio. I risultati ottenuti hanno mostrato come la combinazione del K-method con le analisi nel termico possa completare l’analisi dell’incendio.UnpublishedCentro di Geodesia Spaziale “G. Colombo”, Matera1.10. TTC - Telerilevamentoope
Spectral analysis of ASTER and HYPERION data for geological classification of Volcano Teide.
No full textThis work is an evaluation to which degree geological information can be obtained from modern remote sensing systems like the multispectral ASTER or the hyperspectral Hyperion sensor for a volcanic region like Teide Volcano (Tenerife, Canary Islands). To account for the enhanced information content these sensors provide, hyperspectral analysis methods, incorporating for example Minimum Noise Fraction-Transformation (MNF) for data quality assessment and noise reduction as well as Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) for supervised classification, were applied. Ground Truth reflectance data were obtained with a FieldSpec Pro measurements campaign conducted during later summer of 2007 in the frame of the EC project PREVIEW (http://www.preview-risk.com/).PublishedHonolulu, USA1.10. TTC - Telerilevamentorestricte
Dati iperspettrali nel VIS-SWIR per l’analisi dell’emissione relativa alla banda del Potassio per lo studio di incendi
No full textGli incendi sono un fenomeno che colpisce ogni anno il nostro pianeta ed in particolare anche l’Italia. Oltre all’effetto immediato sul territorio, è stato riconosciuto un effetto a livello di impatto climatico. I fuochi cambiano lo stato fisico della vegetazione rilasciando nell’atmosfera gas che giocano un ruolo importante nell’effetto serra. E’ stato stimato che la biomassa bruciata in un anno, contribuisce al 38% dell’Ozono in troposfera, al 32% di monossido di Carbonio, al 20% degli altri gas (Levine, 1991; Andreae, 1991; Kaufman et al., 1998a,b).
L’uso dei canali termici (8-14 micron) o relativi al vicino infrarosso (1.0 -2.5 micron) sono tradizionalmente utilizzati per la detection e lo studio di parametri fisici come il potere radiante, l’NDBR (Normalised Difference Burn Ratio), o il tasso di combustione della biomassa. Nel visibile una banda di emissione diagnostica dello stato di fiamma, è quella del Potassio (K-method) che fino ad ora non è stata molto studiata in quanto limitata dalle prestazioni degli strumenti. Nell’estate 2006, con il progetto AIRFIRE finanziato da ESA, una campagna aerea è stata effettuata su incendi non controllati utilizzanodo un prototipo di sensore iperspettrale denominato SIM-GA di Selex Galileo. Il SIM-GA è un sensore iperspettrale ad altissima risoluzione spettrale 1.2nm e 2.5 nm rispettivamente nel VISIBILE (350-1200nm) e nel vicino infrarosso(1200-2500nm) che opera in modalità pushbroom. I dati ottenuti hanno permesso di verificare e testare il metodo della panda del Potassio. I risultati ottenuti hanno mostrato come la combinazione del K-method con le analisi nel termico possa completare l’analisi dell’incendio.UnpublishedCentro di Geodesia Spaziale “G. Colombo”, Matera1.10. TTC - Telerilevamentoope
ASTER temperature and emissivity validation on volcano Teide
No full textThe Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER ) has operated since 19 December 1999 from NASA’s Terra Earth-orbiting, sun synchronous satellite. Emissivity and temperature standard products are based on the TES algorithms and require periodical validation campaign. In the frame of the EC project PREVIEW (http://www.preview-risk.com/) a field campaign on Volcano Teide was carried on, from the 16th to 24th of September 2007, to validate and to integrate the satellite derived products services.PublishedHonolulu, USA1.10. TTC - Telerilevamentorestricte
ASTER temperature and emissivity validation on volcano Teide
No full textThe Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER ) has operated since 19 December 1999 from NASA’s Terra Earth-orbiting, sun synchronous satellite. Emissivity and temperature standard products are based on the TES algorithms and require periodical validation campaign. In the frame of the EC project PREVIEW (http://www.preview-risk.com/) a field campaign on Volcano Teide was carried on, from the 16th to 24th of September 2007, to validate and to integrate the satellite derived products services.PublishedHonolulu, USA1.10. TTC - Telerilevamentorestricte
Spectral analysis of ASTER and HYPERION data for geological classification of Volcano Teide
No full textThis work is an evaluation to which degree geological information can be obtained from modern remote sensing systems like the multispectral ASTER or the hyperspectral Hyperion sensor for a volcanic region like Teide Volcano (Tenerife, Canary Islands). To account for the enhanced information content these sensors provide, hyperspectral analysis methods, incorporating for example Minimum Noise Fraction-Transformation (MNF) for data quality assessment and noise reduction as well as Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) for supervised classification, were applied. Ground Truth reflectance data were obtained with a FieldSpec Pro measurements campaign conducted during later summer of 2007 in the frame of the EC project PREVIEW (http://www.preview-risk.com/).PublishedHonolulu, USA1.10. TTC - Telerilevamentopartially_ope
A neural network approach for monitoring of volcanic SO2 and plume height using hyperspectral measurements
No full textIn this study two neural networks were implemented in order to emulate a retrieval model and to estimate the sulphur dioxide (SO2) columnar content and cloud height from volcanic eruption. ANNs were trained using all Infrared Atmospheric Sounding Interferometer (IASI) channels in Thermal Infrared (TIR) as inputs, and the corresponding values of SO2 content and height of volcanic cloud obtained using the Oxford SO2 retrievals as target outputs. The retrieval is demonstrated for the eruption of the Eyjafjallajökull volcano (Iceland) occurred in 2010 and to three IASI images of the Grímsvötn volcanic eruption that occurred in May 2011, in order to evaluate the networks for an unknown eruption. The results of validation, both for Eyjafjallajökull independent data-sets, provided root mean square error (RMSE) values between neural network outputs and targets lower than 20 DU for SO2 total column and 200 mb for cloud height, therefore demonstrating the feasibility to estimate SO2 values using a neural network approach, and its importance in near real time monitoring activities, owing to its fast application. Concerning the validation carried out with neural networks on images from the Grímsvötn eruption, the RMSE of the outputs remained lower than the Standard Deviation (STD) of targets, and the neural network underestimated retrieval only where target outputs showed different statistics than those used during the training phase
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