Chini_NN_IGARSS2011.pptx

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Chini_NN_IGARSS2011.pptx

  1. 1. Volcanic ash retrieval from IR multispectral measurements by means of Neural Networks: an analysis of the Eyjafjallajokull eruption<br />Matteo Picchiani1, Marco Chini2, Stefano Corradini2, Luca Merucci2, Pasquale Sellitto3,Fabio Del Frate1, Alessandro Piscini2 and Salvatore Stramondo2<br />1Earth Observation Laboratory – Tor Vergata University, Rome, Italy<br />2Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy <br />3Laboratoire Inter-universitaire des SystèmesAtmosphériques (LISA), Universités Paris-Est et Paris Diderot, CNRS, Créteil, France <br />
  2. 2. Scenario<br /><ul><li>The tracking of volcanic clouds is a key task for aviation safety, allowing to beware the dangerous effects of fine volcanic ash particles on aircrafts.
  3. 3. The procedure for the ash mass computation [Prata et al., 1989; Wen & Rose, 1994] requires many input parameters and it can be so time consuming that could prevent the utilization during the crisis phases.
  4. 4. A novel technique [1] based on the synergic use of MODTRAN simulations and Neural Network has shown good potentiality in the automatic development of Ash detection and Ash mass retrievals from Moderate resolution Imager Spectroradiometer (MODIS) data.</li></ul>[1] Picchiani, M.,  Chini, M., Corradini, S., Merucci, L., Sellitto, P., Del Frate, F. and Stramondo, S., “Volcanic ash detection and retrievals from MODIS data by means of Neural Networks”, Atmos. Meas. Tech. Discuss., 4, 2567-2598, 2011.<br />
  5. 5. Scenario<br />The methodology has been developed considering several eruption of Mt. Etna [37.73°N, 15.00°E], a massive stratovolcano (3330 m a.s.l.) located in the eastern part of Sicily (Italy), showing interesting results: <br />BTD Ash Retrieval<br />BTD Ash Retrieval<br />NN Ash Retrieval<br />NN Ash Retrieval<br />
  6. 6. Scenario<br />The Eyjafjallajokull volcano, located on the south of Iceland, is a stratovolcano 1666 meters high, with a caldera on its summit, 2.5 km wide. The unexpected explosive activity lasted from April 14th, to May 23rd, 2010 causing widespread and unprecedented disruption to aviation and everyday life in large parts of Europe.<br /><ul><li>A set of MODIS images collected during the Eyjafjallajokull eruption have been analyzed by means of NN algorithm.
  7. 7. The results of NN and BTD has been compared.</li></li></ul><li>Motivations of NN approach:<br /><ul><li>No need for ancillary data.
  8. 8. If the NN is properly trained new data can be inverted in a few minutes (instead of some hours of MODTRAN based procedure).
  9. 9. Possibility to employ a trained NNs to new area under specified conditions (sea surface temperature, atmospheric profile, i.e. similar latitude and longitude).</li></li></ul><li>Problems<br />Problem :<br />VolcanicAsh Detection (discriminate ash from meteorological clouds).<br />Problem :<br />VolcanicAshRetrieval.<br />
  10. 10. ModisSpectralBands<br />MODIS is a multi-spectral instrument that covers 36 spectral bands, from visible (VIS) to thermal infrared (TIR) with a global coverage in 1 to 2 days. The spatial resolution ranges from 250 m to 1000 m, depending on the acquisition mode.<br />
  11. 11. Artificial Neural Networks<br />Artificial Neural Networks (ANNs) can be seen as mathematical models for multivariate nonlinear regression or functional approximation.<br />Functional mapping: a relationship between an input space (the space of the data) and an output space is searched :<br />y= Ψw (x)<br />x : vector of independent variables<br />w : free adjustable parameters<br />In ANNs Ψ is a linear combination of a large number of non-linear functions (sigmoid functions).<br />
  12. 12. Artificial Neural Networks<br />The most popular ANN architecture is the Multilayer Perceptron(MLP):<br />Neurons are organized in layers<br /><ul><li>One input layer, containing the inputs to the net.
  13. 13. One or more hidden layers, consisting of non linear neurons.
  14. 14. One output layer, which produces the output signal.</li></ul>MLP are feedforwardNeural Network:<br />the signal is propagated forward through the layers (no recurrent connections).<br />
  15. 15. Neural Networks Training<br /><ul><li>The training data set consist of pairs {(xi,ti)}, where xi is an input signal and ti is the desired response to that input.
  16. 16. During the training phase, the free parameters of the ANN (weights, biases) are adjusted in order to minimize a cost function, e.g. </li></ul>p=number of training patterns, <br />M=number of output units<br />Problem: We cannot directly measure the ash quantity in the atmosphere.<br />A forward models is needed.<br />
  17. 17. BTD < 0 volcanic ash<br /> BTD > 0 meteo clouds<br />Pixel Area<br />Ash Density<br />Extinction Efficiency Factor<br />Ash retrieval in the TIR spectral range<br />The cloud discrimination is based on Brightness Temperature Difference algorithm [Prata et al., 1989] (+ water vapor correction)<br />BTD = Tb(11m) - Tb(12m)<br />The retrieval is based on computing the simulated inverted arches curves “BTD vs Tb(11m)” varying the AOD (t) and the particles effective radius (re) [Wen and Rose, 1994; Prata et al., 2001]<br />The TOA simulated Radiances LUT has been computed using MODTRAN RTM<br />
  18. 18. Sat. geometry<br />Plume geometry<br />Spectral surface emissivity and temperature<br />P, T, H<br />Volcanic ash <br />Optical<br />Proprties<br />TOA Radiance computation<br />MODTRAN<br />RTM<br />Ri(AOD, re)<br /><ul><li> 9 values of AOD (0 to 10, constant step in a logarithmic scale)
  19. 19. 8 values of re (0.7 to 10 m, constant step in a logarithmic scale)</li></li></ul><li>Data Set<br />Three MODIS images acquired on April the 19th, 2010, May the 6th 2010 and May the 7th 2010 have been considered for this NNs based Eyjafjallajokull eruption analysis.<br />The channel 31 of MODIS, affected by the ash absorption: <br />May 6th, 2010<br />May 7th, 2010<br />April 19th, 2010<br />
  20. 20. Neural Networks Training<br />E<br />Training time<br />When to stop Training?<br />E on Training set<br />E on Test set<br />Training: 65%<br />Test: 20%<br />Validation: 15%<br />A trade off between accuracyandgeneralization capability of the networks are reached when the error function on the test set reaches the global minimum.<br />
  21. 21. Methodology<br /><ul><li>Two different NNs have been trained for the ash detection and retrieval.
  22. 22. Training (Tr), Test (Ts) and Validation (V) sets have been extracted from the data to train the NNs.
  23. 23. Input-output pairs: MODIS Ch 28-31-32 – MODTRAN based procedure results.</li></li></ul><li>Methodology: NN forAsh Detection<br />NN -Inputs<br />Ch. 32<br />Ch. 28<br />Ch. 31<br />BTD<br />NN – Target Outputs<br />
  24. 24. Methodology: NN forAsh Detection<br />Ch 28<br /> 0 1: NotAsh<br />1 0 : Ash<br />CH 31<br />CH 32<br />Uniform Sampling<br />Neural Network forAsh Detection<br />Tr, Ts an V sets have been extracted from the ash plume (Ash class) and the remaining zone of the images (Not Ash class).<br />Inputs:CH 28 CH 31 CH 32<br />Output: Ash Detection Map<br />
  25. 25. Methodology: NN for Ash Retrieval<br />NN -Inputs<br />Ch. 32<br />Ch. 28<br />Ch. 31<br />BTD - MODTRAN<br />NN – Target Outputs<br />
  26. 26. Methodology: NN forAshRetrieval<br />CH 28<br />CH 31<br />CH 32<br />Uniform Sampling<br />Neural Network forAsh Mass Retrieval<br />Tr, Ts an V sets have been extracted from the ash plume.<br />Output: Ash Mass Map<br />Inputs:CH 28 CH 31 CH 32<br />
  27. 27. Methodology: Processing Chain<br />The two NNs have been insert in an automatic chain, processing the MODIS data to produce the ash detection and ash mass retrieved maps. The second NN is applied only where the ash is detected by the first NN. To improve the results a region growing algorithm is applied after the NN for the detection.<br />NN forAsh Detection<br />NN for Ash Mass Retrieval<br />Inputs:CH 28 CH 31 CH 32<br />A region growing approach can be further applied to avoid the false positive ash pixels, due to high meteorological clouds.<br />Ch 28<br />CH 31<br />CH 32<br />
  28. 28. Ash Detection Results<br />Confusion Matrix computed onto the V sets:<br />April 19th 2010<br />
  29. 29. Ash Retrieval Results<br />Scatter plots computed onto the V sets:<br />
  30. 30. NN procedure – MODTRAN based procedure results comparison<br />April 19th 2010<br />BTD – MODTRAN Ash Retrieval<br />NN Ash Retrieval<br />
  31. 31. NN procedure – MODTRAN based procedure results comparison<br />May 6th 2010<br />BTD – MODTRAN Ash Retrieval<br />NN Ash Retrieval<br />
  32. 32. NN procedure – MODTRAN based procedure results comparison<br />May 7th 2010<br />BTD – MODTRAN Ash Retrieval<br />NN Ash Retrieval<br />
  33. 33. The Grismvotn Eruption<br />The eruption events of the Icelandic Grismvotn volcano have offered an interesting opportunity to test the NN procedure. The NNs trained onto Eyjafjallajokull have been used to retrieve the Ash mass of the May 22nd 2011eruption.<br />NN Ash Retrieval<br />BTD – MODTRAN Ash Retrieval<br />
  34. 34. The Grismvotn Eruption<br />BTD – MODTRAN Ash Retrieval<br />NN Ash Retrieval<br />
  35. 35. Conclusion and Future Investigations<br /><ul><li>We investigated the possibility of applying the NNs to the problems of Ash detection and Ash mass retrieval.
  36. 36. A minimum set of MODIS channels have been used.
  37. 37. The obtained results show that the trained NNs can be used on new area under particular conditions (sea surface temperature, atmospheric profile) and can replace the BTD retrieval procedure in the crisis phase management.
  38. 38. Future investigations will concern the study of information content of other MODIS channels to improve the discrimination of meteorological clouds, as well as the inversion of other parameters such as the ash optical thickness (AOT) and the ash effective radius (re).</li></li></ul><li>Thanks for attention.<br />Contact: picchian@disp.uniroma2.it;<br />marco.chini@ingv.it;<br />stefano.corradini@ingv.it <br />

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