WEIBULL MULTIPLICATIVE MODEL AND MACHINE LEARNING MODELS FOR FULL-AUTOMATIC DARK-SPOT DETECTION FROM SAR IMAGES

As a major aspect of marine pollution, oil release into the sea has serious biological and environmental impacts. Among remote sensing systems (which is a tool that offers a non-destructive investigation method), synthetic aperture radar (SAR) can provide valuable synoptic information about the position and size of the oil spill due to its wide area coverage and day/night, and all-weather capabilities. In this paper we present a new automated method for oil-spill monitoring. A new approach is based on the combination of Weibull Multiplicative Model and machine learning techniques to differentiate between dark spots and the background. First, the filter created based on Weibull Multiplicative Model is applied to each sub-image. Second, the sub-image is segmented by two different neural networks techniques (Pulsed Coupled Neural Networks and Multilayer Perceptron Neural Networks). As the last step, a very simple filtering process is used to eliminate the false targets. The proposed approaches were tested on 20 ENVISAT and ERS2 images which contained dark spots. The same parameters were used in all tests. For the overall dataset, the average accuracies of 94.05 % and 95.20 % were obtained for PCNN and MLP methods, respectively. The average computational time for dark-spot detection with a 256 × 256 image in about 4 s for PCNN segmentation 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 to the future spaceborne SAR images

Urban surface temperature time series estimation at the local scale by spatial-spectral unmixing of satellite observations

The study of urban climate requires frequent and accurate monitoring of land surface temperature (LST), at the local scale. Since currently, no space-borne sensor provides frequent thermal infrared imagery at high spatial resolution, the scientific community has focused on synergistic methods for retrieving LST that can be suitable for urban studies. Synergistic methods that combine the spatial structure of visible and near-infrared observations with the more frequent, but low-resolution surface temperature patterns derived by thermal infrared imagery provide excellent means for obtaining frequent LST estimates at the local scale in cities. In this study, a new approach based on spatial-spectral unmixing techniques was developed for improving the spatial resolution of thermal infrared observations and the subsequent LST estimation. The method was applied to an urban area in Crete, Greece, for the time period of one year. The results were evaluated against independent high-resolution LST datasets and found to be very promising, with RMSE less than 2 K in all cases. The developed approach has therefore a high potential to be operationally used in the near future, exploiting the Copernicus Sentinel (2 and 3) observations, to provide high spatio-temporal resolution LST estimates in cities

Multilayer perceptron neural networks model for meteosat second generation SEVIRI daytime cloud masking

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

Limitations in the Use of the Equivalent Diameter

This paper deals with the inaccuracy assessment of the friction pressure loss estimation based on Darcy formula combined with an equivalent hydraulic diameter and a friction factor valid for circular pipes when applied to a square rod bundle. The assessment has been done by comparing the analytical and semi-empirical predictions with two different CFD codes results: CFX and NEPTUNE_CFD. Two different analytical approaches have been considered: the whole-bundle and sub-channel approaches, both for laminar and turbulent flow conditions. Looking at results, it is reasonable to assume that an error in the range of 11% - 23% is likely when using equivalent diameter in the laminar regime. In the case of turbulent regime, the equivalent diameter works better and the error is in the range between a few percent and ~12%

COSMO-SkyMed potential to detect and monitor Mediterranean maquis fires and regrowth: a pilot study in Capo Figari, Sardinia, Italy

Mediterranean maquis is a complex and widespread ecosystem in the region, intrinsically prone to fire. Many species have developed specific adaptation traits to cope with fire, ensuring resistance and resilience. Due to the recent changes in socio-economy and land uses, fires are more and more frequent in the urban-rural fringe and in the coastlines, both now densely populated. The detection of fires and the monitoring of vegetation regrowth is thus of primary interest for local management and for understanding the ecosystem dynamics and processes, also in the light of the recurrent droughts induced by climate change. Among the main objectives of the COSMO-SkyMed radar constellation mission there is the monitoring of environmental hazards; the very high revisiting time of this mission is optimal for post-hazard response activities. However, very few studies exploited such data for fire and vegetation monitoring. In this research, Cosmo-SkyMed is used in a Mediterranean protected area covered by maquis to detect the burnt area extension and to conduct a mid-term assessment of vegetation regrowth. The positive results obtained in this research highlight the importance of the very high-resolution continuous acquisitions and the multi-polarization information provided by COSMO-SkyMed for monitoring fire impacts on vegetation

Neural network multispectral satellite images classification of volcanic ash plumes in a cloudy scenario

This work shows the potential use of neural networks in the characterization of eruptive events monitored by satellite, through fast and automatic classification of multispectral images. The algorithm has been developed for the MODIS instrument and can easily be extended to other similar sensors. Six classes have been defined paying particular attention to image regions that represent the different surfaces that could possibly be found under volcanic ash clouds. Complex cloudy scenarios composed by images collected during the Icelandic eruptions of the Eyjafjallajökull (2010) and Grimsvötn (2011) volcanoes have been considered as test cases. A sensitivity analysis on the MODIS TIR and VIS channels has been performed to optimize the algorithm. The neural network has been trained with the first image of the dataset, while the remaining data have been considered as independent validation sets. Finally, the neural network classifier’s results have been compared with maps classified with several interactive procedures performed in a consolidated operational framework. This comparison shows that the automatic methodology proposed achieves a very promising performance, showing an overall accuracy greater than 84%, for the Eyjafjallajökull event, and equal to 74% for the Grimsvötn event

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

Volcanic ash clouds detection and retrieval represent a key issue for aviation safety due to the harming effects on aircraft. A lesson learned from the recent Eyjafjallajokull eruption is the need to obtain accurate and reliable retrievals on a real time basis. <br><br> In this work we have developed a fast and accurate Neural Network (NN) approach to detect and retrieve volcanic ash cloud properties from the Moderate Resolution Imaging Spectroradiometer (MODIS) data in the Thermal InfraRed (TIR) spectral range. Some measurements collected during the 2001, 2002 and 2006 Mt. Etna volcano eruptions have been considered as test cases. <br><br> The ash detection and retrievals obtained from the Brightness Temperature Difference (BTD) algorithm are used as training for the NN procedure that consists in two separate steps: ash detection and ash mass retrieval. The ash detection is reduced to a classification problem by identifying two classes: "ashy" and "non-ashy" pixels in the MODIS images. Then the ash mass is estimated by means of the NN, replicating the BTD-based model performances. A segmentation procedure has also been tested to remove the false ash pixels detection induced by the presence of high meteorological clouds. The segmentation procedure shows a clear advantage in terms of classification accuracy: the main drawback is the loss of information on ash clouds distal part. <br><br> The results obtained are very encouraging; indeed the ash detection accuracy is greater than 90%, while a mean RMSE equal to 0.365 t km<sup>−2</sup> has been obtained for the ash mass retrieval. Moreover, the NN quickness in results delivering makes the procedure extremely attractive in all the cases when the rapid response time of the system is a mandatory requirement

Unmet needs in ANCA-associated vasculitis: Physicians’ and patients’ perspectives

In recent years, clinical research has increased significantly and therapies for antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis have improved. However, there are still unanswered questions and unmet needs about AAV patients. The purpose of this review is to examine the frontiers of research related to emerging biomarkers eventually predicting relapse, and new therapeutic approaches, not to mention new quality of life assessment tools. Identifying predictors of relapse may help optimize therapeutic strategies, minimize disease recurrence, and reduce treatment-related side effects. In addition, it is important to recognize that patients may suffer long-term consequences of the disease and its treatment, which, although life-saving, is often associated with significant side effects. Our goal, therefore, is to highlight what has been achieved, the pitfalls, and what still needs to be done, comparing the views of physicians and patients