Volcanic ash detection and retrievals using MODIS data by means of neural networks

Picchiani, Matteo; Chini, Marco; Corradini, Stefano; Merucci, Luca; Sellitto, Pasquale; Frate, Fabio Del; Stramondo, Salvatore

doi:10.5194/amt-4-2619-2011

Cited by 37 publications

(36 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Neural networks are powerful tools for the mapping of nonlinear relationships, and have been successfully applied as stand-alone retrieval algorithms to several remote sensing problems, including aerosol-related applications (Han et al, 2006;Niang et al, 2006;Radosavljevic et al, 2010;Picchiani et al, 2011). Nevertheless, the output of a NN retrieval scheme is also suitable for use as the initial step of an iterative retrieval method, giving rise to a "neurovariational" retrieval scheme.…”

Section: Introductionmentioning

confidence: 99%

Use of neural networks in ground-based aerosol retrievals from multi-angle spectropolarimetric observations

et al. 2015

View full text Add to dashboard Cite

Abstract. In this paper, the use of a neural network algorithm for the retrieval of the aerosol properties from groundbased spectropolarimetric measurements is discussed. The neural network is able to retrieve the aerosol properties with an accuracy that is almost comparable to that of an iterative retrieval. By using the outcome of the neural network as first guess in the iterative retrieval scheme, the accuracy of the retrieved fine-and coarse-mode optical thickness is further improved, while for the other parameters the improvement is small or absent. The resulting scheme (neural network + iterative retrieval) is compared to the original one (look-up table + iterative retrieval) on a set of simulated ground-based measurements, and on a small set of real observations carried out by an accurate ground-based spectropolarimeter. The results show that the use of a neural-networkbased first guess leads to an increase in the number of converging retrievals, and possibly to more accurate estimates of the aerosol effective radius and complex refractive index.

show abstract

Section: Introductionmentioning

confidence: 99%

Use of neural networks in ground-based aerosol retrievals from multi-angle spectropolarimetric observations

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Additional benefits are the independence from a priori assumptions about the statistical characterization of the data and the possibility of easily incorporating different types of data (Foody, 1995). For these reasons, NNs have often been successfully used for the solution of the inverse problem of geophysical quantities from satellite measurements (Blackwell, 2005;Picchiani et al, 2011Picchiani et al, , 2012.…”

Section: Neural Networkmentioning

confidence: 99%

“…In this work, we used the same approach developed in Picchiani et al (2011) to train the NNs. The backpropagation algorithm (Bishop, 1995) has been applied performing a cross-validation approach (Haykin, 1994;Bishop, 1995) to avoid the possibility of over training, i.e., the memorization of specific patterns instead of statistical mapping linking the inputs to outputs and, therefore, hampering the generalization capability for new data.…”

Section: Neural Networkmentioning

confidence: 99%

“…These tasks strongly depend on the specific retrieval problem and they need to be addressed and characterized carefully in order to avoid failures: due either to overfitting or to a scarce capability in the determination of the desired input-output relationship. Recently, NNs were applied to a scenario for the detection of ash plume and the retrieval of the ash mass for the Etna volcano scenario, using a topology involving channels centered at 11 µm and 12 µm, plus the water vapor absorption at 7.3 µm (Picchiani et al, 2011). In such a study, the retrieval was only conducted above the sea, since the scenario did not imply the presence of ash and meteorological clouds on the same pixels.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A neural network approach for the simultaneous retrieval of volcanic ash parameters and SO<sub>2</sub> using MODIS data

Piscini

Picchiani

Chini³

et al. 2014

Atmos. Meas. Tech.

Self Cite

View full text Add to dashboard Cite

Abstract. In this work neural networks (NNs) have been used for the retrieval of volcanic ash and sulfur dioxide (SO 2 ) parameters based on Moderate Resolution Imaging Spectroradiometer (MODIS) multispectral measurements. Different neural networks were built in order for each parameter to be retrieved, for experimenting with different topologies and evaluating their performances. The neural networks' capabilities to process a large amount of new data in a very fast way have been exploited to propose a novel applicative scheme aimed at providing a complete characterization of eruptive products.As a test case, the May 2010 Eyjafjallajókull eruption has been considered. A set of seven MODIS images have been used for the training and validation phases.In order to estimate the parameters associated to the volcanic eruption, such as ash mass, effective radius, aerosol optical depth and SO 2 columnar abundance, the neural networks have been trained using the retrievals from wellknown algorithms. These are based on simulated radiances at the top of the atmosphere and are estimated by radiative transfer models.Three neural network topologies with a different number of inputs have been compared: (a) three thermal infrared MODIS channels, (b) all multispectral MODIS channels and (c) the channels selected by a pruning procedure applied to all MODIS channels.Results show that the neural network approach is able to estimate the volcanic eruption parameters very well, showing a root mean square error (RMSE) below the target data standard deviation (SD). The network built considering all the MODIS channels gives a better performance in terms of specialization, mainly on images close in time to the training ones, while the networks with less inputs reveal a better generalization performance when applied to independent data sets. In order to increase the network's generalization capability and to select the most significant MODIS channels, a pruning algorithm has been implemented. The pruning outcomes revealed that channel sensitive to ash parameters correspond to the thermal infrared, visible and mid-infrared spectral ranges.The neural network approach has been proven to be effective when addressing the inversion problem for the estimation of volcanic ash and SO 2 cloud parameters, providing fast and reliable retrievals, important requirements during volcanic crises.

show abstract

“…Furthermore, Iyer et al proposed a classification system based on extracting unique cepstral features from a volcano's infrasonic signature, and feeding these features to the Radial Basis Function Neural Network [32]. Moreover, Picchiani et al used a single layer neural network to classify ash clouds of MODIS images in the Thermal InfraRed (TIR) spectral [33]. Furthermore, Tan et al proposed a Bayesian detection algorithm and a near-optimal sensor selection algorithm to detect earthquake events and timing using wireless sensor networks [34].…”

Section: Related Workmentioning

confidence: 99%

A Novel Vision-Based Classification System for Explosion Phenomena

Abusaleh¹,

Mahmood²,

Elleithy³

et al. 2017

J. Imaging

View full text Add to dashboard Cite

Abstract:The need for a proper design and implementation of adequate surveillance system for detecting and categorizing explosion phenomena is nowadays rising as a part of the development planning for risk reduction processes including mitigation and preparedness. In this context, we introduce state-of-the-art explosions classification using pattern recognition techniques. Consequently, we define seven patterns for some of explosion and non-explosion phenomena including: pyroclastic density currents, lava fountains, lava and tephra fallout, nuclear explosions, wildfires, fireworks, and sky clouds. Towards the classification goal, we collected a new dataset of 5327 2D RGB images that are used for training the classifier. Furthermore, in order to achieve high reliability in the proposed explosion classification system and to provide multiple analysis for the monitored phenomena, we propose employing multiple approaches for feature extraction on images including texture features, features in the spatial domain, and features in the transform domain. Texture features are measured on intensity levels using the Principal Component Analysis (PCA) algorithm to obtain the highest 100 eigenvectors and eigenvalues. Moreover, features in the spatial domain are calculated using amplitude features such as the YC b C r color model; then, PCA is used to reduce vectors' dimensionality to 100 features. Lastly, features in the transform domain are calculated using Radix-2 Fast Fourier Transform (Radix-2 FFT), and PCA is then employed to extract the highest 100 eigenvectors. In addition, these textures, amplitude and frequency features are combined in an input vector of length 300 which provides a valuable insight into the images under consideration. Accordingly, these features are fed into a combiner to map the input frames to the desired outputs and divide the space into regions or categories. Thus, we propose to employ one-against-one multi-class degree-3 polynomial kernel Support Vector Machine (SVM). The efficiency of the proposed research methodology was evaluated on a totality of 980 frames that were retrieved from multiple YouTube videos. These videos were taken in real outdoor environments for the seven scenarios of the respective defined classes. As a result, we obtained an accuracy of 94.08%, and the total time for categorizing one frame was approximately 0.12 s.

show abstract

Volcanic ash detection and retrievals using MODIS data by means of neural networks

Cited by 37 publications

References 54 publications

Use of neural networks in ground-based aerosol retrievals from multi-angle spectropolarimetric observations

Use of neural networks in ground-based aerosol retrievals from multi-angle spectropolarimetric observations

A neural network approach for the simultaneous retrieval of volcanic ash parameters and SO<sub>2</sub> using MODIS data

A Novel Vision-Based Classification System for Explosion Phenomena

Contact Info

Product

Resources

About

Volcanic ash detection and retrievals using MODIS data by means of neural networks

Cited by 37 publications

References 54 publications

Use of neural networks in ground-based aerosol retrievals from multi-angle spectropolarimetric observations

Use of neural networks in ground-based aerosol retrievals from multi-angle spectropolarimetric observations

A neural network approach for the simultaneous retrieval of volcanic ash parameters and SO&lt;sub&gt;2&lt;/sub&gt; using MODIS data

A Novel Vision-Based Classification System for Explosion Phenomena

Contact Info

Product

Resources

About

A neural network approach for the simultaneous retrieval of volcanic ash parameters and SO<sub>2</sub> using MODIS data