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Dr. Alireza Taravat
Deimos Space UK Ltd, Building R103, Fermi Avenue, Harwell, Oxford, OX11 0QR, UK

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0 Change Detection
0 Classification
0 Deep Learning
0 Forest Management
0 Machine Learning

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Machine Learning

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Technical note
Published: 16 February 2021 in Remote Sensing
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Accurate spatial information of agricultural fields is important for providing actionable information to farmers, managers, and policymakers. On the other hand, the automated detection of field boundaries is a challenging task due to their small size, irregular shape and the use of mixed-cropping systems making field boundaries vaguely defined. In this paper, we propose a strategy for field boundary detection based on the fully convolutional network architecture called ResU-Net. The benefits of this model are two-fold: first, residual units ease training of deep networks. Second, rich skip connections within the network could facilitate information propagation, allowing us to design networks with fewer parameters but better performance in comparison with the traditional U-Net model. An extensive experimental analysis is performed over the whole of Denmark using Sentinel-2 images and comparing several U-Net and ResU-Net field boundary detection algorithms. The presented results show that the ResU-Net model has a better performance with an average F1 score of 0.90 and average Jaccard coefficient of 0.80 in comparison to the U-Net model with an average F1 score of 0.88 and an average Jaccard coefficient of 0.77.

ACS Style

Alireza Taravat; Matthias Wagner; Rogerio Bonifacio; David Petit. Advanced Fully Convolutional Networks for Agricultural Field Boundary Detection. Remote Sensing 2021, 13, 722 .

AMA Style

Alireza Taravat, Matthias Wagner, Rogerio Bonifacio, David Petit. Advanced Fully Convolutional Networks for Agricultural Field Boundary Detection. Remote Sensing. 2021; 13 (4):722.

Chicago/Turabian Style

Alireza Taravat; Matthias Wagner; Rogerio Bonifacio; David Petit. 2021. "Advanced Fully Convolutional Networks for Agricultural Field Boundary Detection." Remote Sensing 13, no. 4: 722.

Journal article
Published: 10 February 2020 in ISPRS International Journal of Geo-Information
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A growing world population, increasing prosperity in emerging countries, and shifts in energy and food demands necessitate a continuous increase in global agricultural production. Simultaneously, risks of extreme weather events and a slowing productivity growth in recent years has caused concerns about meeting the demands in the future. Crop monitoring and timely yield predictions are an important tool to mitigate risk and ensure food security. A common approach is to combine the temporal simulation of dynamic crop models with a geospatial component by assimilating remote sensing data. To ensure reliable assimilation, handling of uncertainties in both models and the assimilated input data is crucial. Here, we present a new approach for data assimilation using particle swarm optimization (PSO) in combination with statistical distance metrics that allow for flexible handling of model and input uncertainties. We explored the potential of the newly proposed method in a case study by assimilating canopy cover (CC) information, obtained from Sentinel-2 data, into the AquaCrop-OS model to improve winter wheat yield estimation on the pixel- and field-level and compared the performance with two other methods (simple updating and extended Kalman filter). Our results indicate that the performance of the new method is superior to simple updating and similar or better than the extended Kalman filter updating. Furthermore, it was particularly successful in reducing bias in yield estimation.

ACS Style

Matthias P. Wagner; Thomas Slawig; Alireza Taravat; Natascha Oppelt. Remote Sensing Data Assimilation in Dynamic Crop Models Using Particle Swarm Optimization. ISPRS International Journal of Geo-Information 2020, 9, 105 .

AMA Style

Matthias P. Wagner, Thomas Slawig, Alireza Taravat, Natascha Oppelt. Remote Sensing Data Assimilation in Dynamic Crop Models Using Particle Swarm Optimization. ISPRS International Journal of Geo-Information. 2020; 9 (2):105.

Chicago/Turabian Style

Matthias P. Wagner; Thomas Slawig; Alireza Taravat; Natascha Oppelt. 2020. "Remote Sensing Data Assimilation in Dynamic Crop Models Using Particle Swarm Optimization." ISPRS International Journal of Geo-Information 9, no. 2: 105.

Journal article
Published: 25 March 2019 in Remote Sensing
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Grassland contributes to carbon storage and animal feed production. Its yield is largely determined by the cutting times of grassland. Previous studies have used remote sensing data for grassland biomass estimation, but only a few studies have focused on SAR remote sensing approaches for automatic grassland cutting status detection. Due to the occurrence of multiple cuttings in a year, it is crucial to effectively monitor grassland cutting events in order to achieve accurate biomass estimations of a whole season. In this study, we examined the capabilities of multilayer perceptron neural networks for automatic grassland cutting status detection using SAR imagery. The proposed model inputs are a time series dataset of VV and VH Sentinel-1 C-band SAR and second-order texture metrics (homogeneity, entropy, contrast and dissimilarity). The proposed approach has been successfully tested on a dataset collected from several fields in Germany in 2016, with an overall accuracy of 85.71% for the validation set.

ACS Style

Alireza Taravat; Matthias P. Wagner; Natascha Oppelt. Automatic Grassland Cutting Status Detection in the Context of Spatiotemporal Sentinel-1 Imagery Analysis and Artificial Neural Networks. Remote Sensing 2019, 11, 711 .

AMA Style

Alireza Taravat, Matthias P. Wagner, Natascha Oppelt. Automatic Grassland Cutting Status Detection in the Context of Spatiotemporal Sentinel-1 Imagery Analysis and Artificial Neural Networks. Remote Sensing. 2019; 11 (6):711.

Chicago/Turabian Style

Alireza Taravat; Matthias P. Wagner; Natascha Oppelt. 2019. "Automatic Grassland Cutting Status Detection in the Context of Spatiotemporal Sentinel-1 Imagery Analysis and Artificial Neural Networks." Remote Sensing 11, no. 6: 711.

Social science
Published: 01 January 2017 in Journal of Maps
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Land-use dynamic is a major challenge for town and country planners especially in developing countries such as Iran. Iran has been under rapid urban expansion and population growth for past three decades which led to lack of resources, environmental deterioration and haphazard landscape development. In this paper, an attempt has been made to map the urbanization dynamics of Tehran in 40 years based on remote sensing imagery and by means of artificial neural networks. The presented scheme could be taken into consideration when planning initiatives aimed at surveying, monitoring, managing and sustainable development of the territory. Moreover, it can serve the experts in the fields of geography, urban studies and planning as a background for number of geographical analyses.

ACS Style

Alireza Taravat; Masih Rajaei; Iraj Emadodin. Urbanization dynamics of Tehran city (1975–2015) using artificial neural networks. Journal of Maps 2017, 13, 24 -30.

AMA Style

Alireza Taravat, Masih Rajaei, Iraj Emadodin. Urbanization dynamics of Tehran city (1975–2015) using artificial neural networks. Journal of Maps. 2017; 13 (1):24-30.

Chicago/Turabian Style

Alireza Taravat; Masih Rajaei; Iraj Emadodin. 2017. "Urbanization dynamics of Tehran city (1975–2015) using artificial neural networks." Journal of Maps 13, no. 1: 24-30.

Journal article
Published: 20 November 2016 in International Journal of Remote Sensing
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In this article, a novel technique based on artificial neural networks (NN) is proposed for cloud coverage short-term forecasting (nowcasting). In particular, the capabilities of multi-layer perceptron NN and time series analysis with nonlinear autoregressive with exogenous input NN are explored and applied to the European meteorological system ‘Meteosat Second Generation’ with its payload Spinning Enhanced Visible and InfraRed Imager. The general neural architecture consists of a first stage addressing the prediction of the radiance images at six bands (0.6, 0.8, 1.6, 3.9, 6.2 and 10.8 μm). In a second stage a cloud masking algorithm, always based on NN, is applied to the predicted images for the cloud coverage nowcasting. The scheme was compared with the most basic forecast algorithm for the prediction: the persistent model. Two test areas characterized by different climatology have been considered for the performance analysis. The results show that about 85% of the changes occurring in the time window were recognized by the proposed technique.

ACS Style

Simone Peronaci; Alireza Taravat; Fabio Del Frate; Natascha Oppelt. Use of NARX neural networks for Meteosat Second Generation SEVIRI very short-term cloud mask forecasting. International Journal of Remote Sensing 2016, 37, 6205 -6215.

AMA Style

Simone Peronaci, Alireza Taravat, Fabio Del Frate, Natascha Oppelt. Use of NARX neural networks for Meteosat Second Generation SEVIRI very short-term cloud mask forecasting. International Journal of Remote Sensing. 2016; 37 (24):6205-6215.

Chicago/Turabian Style

Simone Peronaci; Alireza Taravat; Fabio Del Frate; Natascha Oppelt. 2016. "Use of NARX neural networks for Meteosat Second Generation SEVIRI very short-term cloud mask forecasting." International Journal of Remote Sensing 37, no. 24: 6205-6215.

Journal article
Published: 25 October 2016 in Water
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Lake Urmia, the second largest saline Lake on earth and a highly endangered ecosystem, is on the brink of a serious environmental disaster similar to the catastrophic death of the Aral Sea. Progressive drying has been observed during the last decade, causing dramatic changes to Lake Urmia’s surface and its regional water supplies. The present study aims to improve monitoring of spatiotemporal changes of Lake Urmia in the period 1975–2015 using the multi-temporal satellite altimetry and Landsat (5-TM, 7-ETM+ and 8-OLI) images. In order to demonstrate the impacts of climate change and human pressure on the variations in surface extent and water level, Lake Sevan and Van Lake with different characteristics were studied along with the Urmia Lake. Normalized Difference Water Index-Principal Components Index (NDWI-PCs), Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Normalized Difference Moisture Index (NDMI), Water Ratio Index (WRI), Normalized Difference Vegetation Index (NDVI), Automated Water Extraction Index (AWEI), and MultiLayer Perceptron Neural Networks (MLP NNs) classifier were investigated for the extraction of surface water from Landsat data. The presented results revealed that MLP NNs has a better performance in the cases where the other models generate poor accuracy. The results show that the area of Lake Sevan and Van Lake have increased while the area of Lake Urmia has decreased by ~65.23% in the past decades, far more than previously reported (~25% to 50%). Urmia Lake’s shoreline has been receding severely between 2010 and 2015 with no sign of recovery, which has been partly blamed on prolonged droughts, aggressive regional water resources development plans, intensive agricultural activities, and anthropogenic changes to the system. The results also indicated that (among the proposed factors) changes in inflows due to overuse of surface water resources and constructing dams (mostly during 1995–2005) are the main reasons for Urmia Lake’s shoreline receding. The model presented in this manuscript can be used by managers as a decision support system to find the effects of building new dams or other infrastructures.

ACS Style

Alireza Taravat; Masih Rajaei; Iraj Emadodin; Hamidreza Hasheminejad; Rahman Mousavian; Ehsan Biniyaz. A Spaceborne Multisensory, Multitemporal Approach to Monitor Water Level and Storage Variations of Lakes. Water 2016, 8, 478 .

AMA Style

Alireza Taravat, Masih Rajaei, Iraj Emadodin, Hamidreza Hasheminejad, Rahman Mousavian, Ehsan Biniyaz. A Spaceborne Multisensory, Multitemporal Approach to Monitor Water Level and Storage Variations of Lakes. Water. 2016; 8 (11):478.

Chicago/Turabian Style

Alireza Taravat; Masih Rajaei; Iraj Emadodin; Hamidreza Hasheminejad; Rahman Mousavian; Ehsan Biniyaz. 2016. "A Spaceborne Multisensory, Multitemporal Approach to Monitor Water Level and Storage Variations of Lakes." Water 8, no. 11: 478.

Conference paper
Published: 01 July 2015 in 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
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In this paper a new approach from the combination of band ratioing function and MLP Neural Networks technique is proposed to differentiate between clouds and background in Landsat ETM+ and MSG SEVIRI data. First, in order to increase the contrast of the clouds and background, a band ratioing function is applied to each sub-image. Second, the sub-images are segmented by MLP Neural Networks technique. The proposed approach was tested on 40 Landsat ETM+ sub-images of Gulf of Mexico and on 40 MSG SEVIRI sub-images over Italy. The same parameters were used in all tests. For the overall dataset, the average accuracy of 89 % was obtained for Landsat ETM+ images and the average accuracy of 85 % was obtained for MSG SEVIRI images. Our experimental results demonstrate that the proposed approach is robust and effective.

ACS Style

Alireza Taravat; Simone Peronaci; Massimilliano Sist; Fabio Del Frate; Natascha Oppelt. The combination of band ratioing techniques and neural networks algorithms for MSG SEVIRI and Landsat ETM+ cloud masking. 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2015, 2315 -2318.

AMA Style

Alireza Taravat, Simone Peronaci, Massimilliano Sist, Fabio Del Frate, Natascha Oppelt. The combination of band ratioing techniques and neural networks algorithms for MSG SEVIRI and Landsat ETM+ cloud masking. 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2015; ():2315-2318.

Chicago/Turabian Style

Alireza Taravat; Simone Peronaci; Massimilliano Sist; Fabio Del Frate; Natascha Oppelt. 2015. "The combination of band ratioing techniques and neural networks algorithms for MSG SEVIRI and Landsat ETM+ cloud masking." 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) , no. : 2315-2318.

Journal article
Published: 01 June 2015 in Journal of Solar Energy Engineering
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In this paper, several models to forecast the hourly solar irradiance with a day in advance using artificial neural network techniques have been developed and analyzed. The forecast irradiance is the one incident on the plane of the modules array of a photovoltaic plant. Pure statistical (ST) models that use only local measured data and model output statistics (MOS) approaches to refine numerical weather prediction data are tested for the University of Rome “Tor Vergata” site. The performance of ST and MOS, together with the persistence model (PM), is compared. The ST models improve the performance of the PM of around 20%. The combination of ST and NWP in the MOS approach gives the best performance, improving the forecast of approximately 39% with respect to the PM.

ACS Style

Cristina Cornaro; F. Bucci; M. Pierro; F. Del Frate; Simone Peronaci; Alireza Taravat. Twenty-Four Hour Solar Irradiance Forecast Based on Neural Networks and Numerical Weather Prediction. Journal of Solar Energy Engineering 2015, 137, 031011 .

AMA Style

Cristina Cornaro, F. Bucci, M. Pierro, F. Del Frate, Simone Peronaci, Alireza Taravat. Twenty-Four Hour Solar Irradiance Forecast Based on Neural Networks and Numerical Weather Prediction. Journal of Solar Energy Engineering. 2015; 137 (3):031011.

Chicago/Turabian Style

Cristina Cornaro; F. Bucci; M. Pierro; F. Del Frate; Simone Peronaci; Alireza Taravat. 2015. "Twenty-Four Hour Solar Irradiance Forecast Based on Neural Networks and Numerical Weather Prediction." Journal of Solar Energy Engineering 137, no. 3: 031011.

Journal article
Published: 02 February 2015 in Remote Sensing
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A multilayer perceptron neural network cloud mask for Meteosat Second Generation SEVIRI (Spinning Enhanced Visible and Infrared Imager) images is introduced and evaluated. The model is trained for cloud detection on MSG SEVIRI daytime data. It consists of a multi-layer perceptron with one hidden sigmoid layer, trained with the error back-propagation algorithm. The model is fed by six bands of MSG data (0.6, 0.8, 1.6, 3.9, 6.2 and 10.8 μm) with 10 hidden nodes. The multiple-layer perceptrons lead to a cloud detection accuracy of 88.96%, when trained to map two predefined values that classify cloud and clear sky. The network was further evaluated using sixty MSG images taken at different dates. The network detected not only bright thick clouds but also thin or less bright clouds. The analysis demonstrated the feasibility of using machine learning models of cloud detection in MSG SEVIRI imagery.

ACS Style

Alireza Taravat; Simon Proud; Simone Peronaci; Fabio Del Frate; Natascha Oppelt. Multilayer Perceptron Neural Networks Model for Meteosat Second Generation SEVIRI Daytime Cloud Masking. Remote Sensing 2015, 7, 1529 -1539.

AMA Style

Alireza Taravat, Simon Proud, Simone Peronaci, Fabio Del Frate, Natascha Oppelt. Multilayer Perceptron Neural Networks Model for Meteosat Second Generation SEVIRI Daytime Cloud Masking. Remote Sensing. 2015; 7 (2):1529-1539.

Chicago/Turabian Style

Alireza Taravat; Simon Proud; Simone Peronaci; Fabio Del Frate; Natascha Oppelt. 2015. "Multilayer Perceptron Neural Networks Model for Meteosat Second Generation SEVIRI Daytime Cloud Masking." Remote Sensing 7, no. 2: 1529-1539.

Journal article
Published: 02 December 2014 in Sensors
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Oil spills represent a major threat to ocean ecosystems and their environmental status. Previous studies have shown that Synthetic Aperture Radar (SAR), as its recording is independent of clouds and weather, can be effectively used for the detection and classification of oil spills. Dark formation detection is the first and critical stage in oil-spill detection procedures. In this paper, a novel approach for automated dark-spot detection in SAR imagery is presented. A new approach from the combination of adaptive Weibull Multiplicative Model (WMM) and MultiLayer Perceptron (MLP) neural networks is proposed to differentiate between dark spots and the background. The results have been compared with the results of a model combining non-adaptive WMM and pulse coupled neural networks. The presented approach overcomes the non-adaptive WMM filter setting parameters by developing an adaptive WMM model which is a step ahead towards a full automatic dark spot detection. The proposed approach was tested on 60 ENVISAT and ERS2 images which contained dark spots. For the overall dataset, an average accuracy of 94.65% was obtained. Our experimental results demonstrate that the proposed approach is very robust and effective where the non-adaptive WMM & pulse coupled neural network (PCNN) model generates poor accuracies.

ACS Style

Alireza Taravat; Natascha Oppelt. Adaptive Weibull Multiplicative Model and Multilayer Perceptron Neural Networks for Dark-Spot Detection from SAR Imagery. Sensors 2014, 14, 22798 -22810.

AMA Style

Alireza Taravat, Natascha Oppelt. Adaptive Weibull Multiplicative Model and Multilayer Perceptron Neural Networks for Dark-Spot Detection from SAR Imagery. Sensors. 2014; 14 (12):22798-22810.

Chicago/Turabian Style

Alireza Taravat; Natascha Oppelt. 2014. "Adaptive Weibull Multiplicative Model and Multilayer Perceptron Neural Networks for Dark-Spot Detection from SAR Imagery." Sensors 14, no. 12: 22798-22810.

Journal article
Published: 29 September 2014 in IEEE Geoscience and Remote Sensing Letters
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Clouds are one of the most important meteorological phenomena affecting the Earth radiation balance. The increasing development of whole-sky images enables temporal and spatial high-resolution sky observations and provides the possibility to understand and quantify cloud effects more accurately. In this letter, an attempt has been made to examine the machine learning [multilayer perceptron (MLP) neural networks and support vector machine (SVM)] capabilities for automatic cloud detection in whole-sky images. The approaches have been tested on a significant number of whole-sky images (containing a variety of cloud overages in different seasons and at different daytimes) from Vigna di Valle and Tor Vergata test sites, located near Rome. The pixel values of red, green, and blue bands of the images have been used as inputs of the mentioned models, while the outputs provided classified pixels in terms of cloud coverage or others (cloud-free pixels and sun). For the test data set, the overall accuracies of 95.07%, with a standard deviation of 3.37, and 93.66%, with a standard deviation of 4.45, have been obtained from MLP neural networks and SVM models, respectively. Although the two approaches generally generate similar accuracies, the MLP neural networks gave a better performance in some specific cases where the SVM generates poor accuracy.

ACS Style

Alireza Taravat; Fabio Del Frate; Cristina Cornaro; Stefania Vergari. Neural Networks and Support Vector Machine Algorithms for Automatic Cloud Classification of Whole-Sky Ground-Based Images. IEEE Geoscience and Remote Sensing Letters 2014, 12, 666 -670.

AMA Style

Alireza Taravat, Fabio Del Frate, Cristina Cornaro, Stefania Vergari. Neural Networks and Support Vector Machine Algorithms for Automatic Cloud Classification of Whole-Sky Ground-Based Images. IEEE Geoscience and Remote Sensing Letters. 2014; 12 (3):666-670.

Chicago/Turabian Style

Alireza Taravat; Fabio Del Frate; Cristina Cornaro; Stefania Vergari. 2014. "Neural Networks and Support Vector Machine Algorithms for Automatic Cloud Classification of Whole-Sky Ground-Based Images." IEEE Geoscience and Remote Sensing Letters 12, no. 3: 666-670.

Conference paper
Published: 17 October 2013 in SPIE Remote Sensing
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ACS Style

Fabio Del Frate; Daniele Latini; Alireza Taravat; Cathleen E. Jones. A novel multi-band SAR data technique for fully automatic oil spill detection in the ocean. SPIE Remote Sensing 2013, 889105 -889105-6.

AMA Style

Fabio Del Frate, Daniele Latini, Alireza Taravat, Cathleen E. Jones. A novel multi-band SAR data technique for fully automatic oil spill detection in the ocean. SPIE Remote Sensing. 2013; ():889105-889105-6.

Chicago/Turabian Style

Fabio Del Frate; Daniele Latini; Alireza Taravat; Cathleen E. Jones. 2013. "A novel multi-band SAR data technique for fully automatic oil spill detection in the ocean." SPIE Remote Sensing , no. : 889105-889105-6.

Journal article
Published: 12 July 2013 in IEEE Transactions on Geoscience and Remote Sensing
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Dark-spot detection is a critical step in oil-spill detection. In this paper, a novel approach for automated dark-spot detection using synthetic aperture radar imagery is presented. A new approach from the combination of Weibull multiplicative model (WMM) and pulse-coupled neural network (PCNN) techniques is proposed to differentiate between the dark spots and the background. First, the filter created based on WMM is applied to each subimage. Second, the subimage is segmented by PCNN techniques. As the last step, a very simple filtering process is used to eliminate the false targets. The proposed approach was tested on 60 Envisat and ERS2 images which contained dark spots. The same parameters were used in all tests. For the overall data set, an average accuracy of 93.66% was obtained. The average computational time for dark-spot detection with a 512 × 512 image is about 7 s using IDL software, which is the fastest one in this field at present. Our experimental results demonstrate that the proposed approach is very fast, robust, and effective. The proposed approach can be applied on any kind of synthetic aperture radar imagery.

ACS Style

Alireza Taravat; Daniele Latini; Fabio Del Frate. Fully Automatic Dark-Spot Detection From SAR Imagery With the Combination of Nonadaptive Weibull Multiplicative Model and Pulse-Coupled Neural Networks. IEEE Transactions on Geoscience and Remote Sensing 2013, 52, 2427 -2435.

AMA Style

Alireza Taravat, Daniele Latini, Fabio Del Frate. Fully Automatic Dark-Spot Detection From SAR Imagery With the Combination of Nonadaptive Weibull Multiplicative Model and Pulse-Coupled Neural Networks. IEEE Transactions on Geoscience and Remote Sensing. 2013; 52 (5):2427-2435.

Chicago/Turabian Style

Alireza Taravat; Daniele Latini; Fabio Del Frate. 2013. "Fully Automatic Dark-Spot Detection From SAR Imagery With the Combination of Nonadaptive Weibull Multiplicative Model and Pulse-Coupled Neural Networks." IEEE Transactions on Geoscience and Remote Sensing 52, no. 5: 2427-2435.

Journal article
Published: 10 May 2012 in EURASIP Journal on Advances in Signal Processing
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Accurate knowledge of the spatial extents and distributions of an oil spill is very impor-tant for efficient response. This is because most petroleum products spread rapidly on the water surface when released into the ocean, with the majority of the affected area becoming covered by very thin sheets. This article presents a study for examining the feasibility of Landsat ETM+ images in order to detect oil spills pollutions. The Landsat ETM+ images for 1st, 10th, 17th May 2010 were used to study the oil spill in Gulf of Mexico. In this article, an attempt has been made to perform ratio operations to enhance the feature. The study concluded that the bands difference between 660 and 560 nm, division at 660 and 560 and division at 825 and 560 nm, normalized by 480 nm provide the best result. Multilayer perceptron neural network classifier is used in order to perform a pixel-based supervised classification. The result indicates the potential of Landsat ETM+ data in oil spill detection. The promising results achieved encourage a further analysis of the potential of the optical oil spill detection approach.

ACS Style

Alireza Taravat; Fabio Del Frate. Development of band ratioing algorithms and neural networks to detection of oil spills using Landsat ETM+ data. EURASIP Journal on Advances in Signal Processing 2012, 2012, 1 .

AMA Style

Alireza Taravat, Fabio Del Frate. Development of band ratioing algorithms and neural networks to detection of oil spills using Landsat ETM+ data. EURASIP Journal on Advances in Signal Processing. 2012; 2012 (1):1.

Chicago/Turabian Style

Alireza Taravat; Fabio Del Frate. 2012. "Development of band ratioing algorithms and neural networks to detection of oil spills using Landsat ETM+ data." EURASIP Journal on Advances in Signal Processing 2012, no. 1: 1.