José Manuel Cotos

Adaptive thresholding algorithm based on SAR images and wind data to segment oil spills along the northwest coast of the Iberian Peninsula.

Satellite Synthetic Aperture Radar (SAR) has been established as a useful tool for detecting hydrocarbon spillage on the oceans surface. Several surveillance applications have been developed based on this technology. Environmental variables such as wind speed should be taken into account for better SAR image segmentation. This paper presents an adaptive thresholding algorithm for detecting oil spills based on SAR data and a wind field estimation as well as its implementation as a part of a functional prototype. The algorithm was adapted to an important shipping route off the Galician coast (northwest Iberian Peninsula) and was developed on the basis of confirmed oil spills. Image testing revealed 99.93% pixel labelling accuracy. By taking advantage of multi-core processor architecture, the prototype was optimized to get a nearly 30% improvement in processing time.

Optimisation of fishing predictions by means of artificial neural networks, anfis, functional networks and remote sensing images

This article presents the application of various Artificial Intelligence techniques to images that proceed from Remote Sensing and serve to predict Prionace Glauca captures (the Prionace Glauca is a type of shark). Our data proceed from remote sensors whose spectral signature allows us to calculate products that are useful for ecological modelling. After digitally processing the Remote Sensing images, we created a database from which to extract the necessary patterns for the training of the artificial neural networks (Backpropagation network, RBF, functional separability network) and the neuro-diffuse networks (ANFIS). These data are used for the training of our system with the aforementioned algorithms. The results show that for this type of problems the generalisation capacity of the functional networks is reduced, which is probably due to the absence of a subjacent mathematical model. Finally, the implementation was carried out with a multilayer perceptron that was trained with a Backpropagation algorithm (error backpropagation); a method that is less complicated than the ANFIS and RBF networks.

Automatic decision support system based on SAR data for oil spill detection

Global trade is mainly supported by maritime transport, which generates important pollution problems. Thus, effective surveillance and intervention means are necessary to ensure proper response to environmental emergencies. Synthetic Aperture Radar (SAR) has been established as a useful tool for detecting hydrocarbon spillages on the oceans surface. Several decision support systems have been based on this technology. This paper presents an automatic oil spill detection system based on SAR data which was developed on the basis of confirmed spillages and it was adapted to an important international shipping route off the Galician coast (northwest Iberian Peninsula). The system was supported by an adaptive segmentation process based on wind data as well as a shape oriented characterization algorithm. Moreover, two classifiers were developed and compared. Thus, image testing revealed up to 95.1% candidate labeling accuracy. Shared-memory parallel programming techniques were used to develop algorithms in order to improve above 25% of the system processing time. HighlightsAn automatic oil spill detection system based on SAR images was developed.A database with confirmed oil spills was used to develop the system.Image testing revealed up to 95.1% candidate labeling accuracy.Two classifiers were compared from the labeling accuracy viewpoint.The processing time was optimized via shared memory parallelization techniques.

Virtual integration of sensor observation data

This paper discusses the design, implementation and evaluation of a framework that enables the virtual integration of heterogeneous observation data sources through a Sensor Observation Service (SOS) standard interface. Currently available SOS implementations follow a data warehouse design approach for data integration. Contrary to this, the present framework uses a well-known Mediator/Wrapper virtual data integration architecture, enabling the direct access to the current data supplied by the data sources. Currently, the framework is being validated as the OGC compliant technology to publish the meteorological and oceanographic observation data generated by two public agencies of the regional government of Galicia (Northwest of Spain). HighlightsVirtual observation data integration vs. prevailing data warehouse approaches.Flexible combination of Mediator/Wrapper architecture with OGC SWE interfaces.In situ and remote static and mobile sensors producing vector and raster data.Multi-thread implementation to leverage current hardware multicore architectures.Under validation in two Spanish public meteorological and oceanographic agencies.

Retelab: A geospatial grid web laboratory for the oceanographic research community

Retelab is a virtual laboratory for the Oceanographic research community. It is supported by a Grid infrastructure and its main objective is to provide an easy and useful tool for oceanographers, where computer skills are not an obstacle. To achieve these goals, Retelab includes improved versions of portal and Grid technologies related to security, data access, and job management. A solution based on a Role Access Management Model has been built for user access and registration, looking for a balance between simplicity and robustness. The sharing and discovery of scientific data is accomplished using a virtual database focused on metadata and designed specifically to store geospatial information. Finally, a comfortable and transparent procedure to submit and to monitor jobs has been developed. It is based on the integration and adaptation of the GridWay metascheduler to the multiuser portal environment in such a way that a single UNIX account can use several proxy certificates. The Virtual Laboratory has been tested by the implementation and deployment of several oceanographic applications.

On the use of feature selection to improve the detection of sea oil spills in SAR images

Fast and effective oil spill detection systems are crucial to ensure a proper response to environmental emergencies caused by hydrocarbon pollution on the oceans surface. Typically, these systems uncover not only oil spills, but also a high number of look-alikes. The feature extraction is a critical and computationally intensive phase where each detected dark spot is independently examined. Traditionally, detection systems use an arbitrary set of features to discriminate between oil spills and look-alikes phenomena. However, Feature Selection (FS) methods based on Machine Learning (ML) have proved to be very useful in real domains for enhancing the generalization capabilities of the classifiers, while discarding the existing irrelevant features. In this work, we present a generic and systematic approach, based on FS methods, for choosing a concise and relevant set of features to improve the oil spill detection systems. We have compared five FS methods: Correlation-based feature selection (CFS), Consistency-based filter, Information Gain, ReliefF and Recursive Feature Elimination for Support Vector Machine (SVM-RFE). They were applied on a 141-input vector composed of features from a collection of outstanding studies. Selected features were validated via a Support Vector Machine (SVM) classifier and the results were compared with previous works. Test experiments revealed that the classifier trained with the 6-input feature vector proposed by SVM-RFE achieved the best accuracy and Cohens kappa coefficient (87.1% and 74.06% respectively). This is a smaller feature combination with similar or even better classification accuracy than previous works. The presented finding allows to speed up the feature extraction phase without reducing the classifier accuracy. Experiments also confirmed the significance of the geometrical features since 75.0% of the different features selected by the applied FS methods as well as 66.67% of the proposed 6-input feature vector belong to this category. HighlightsFive feature selection methods were applied to improve oil spill detection systems.Feature selection discarded irrelevant features and improved the classifier accuracy.The SVM-RFE feature selection method obtained the best accuracy results.A 6-input SVM classifier showed an accuracy of 87.1% and a Kappa statistic of 74.06%.75.0% of the unique selected features belong to the geometrical category.

GeoDADIS: A framework for the development of geographic data acquisition and dissemination servers

The homogeneous access to sensor data in data monitoring and analysis applications is gaining much interest nowadays. To tackle this problem from an application independent perspective, the design and implementation of a framework called GeoDADIS for the development of data acquisition and dissemination servers is discussed in the present paper. Those servers are of common use in monitoring applications as they perform as gateways between decision support and data visualization technologies used in application developments and the heterogeneous collection of protocols and interfaces available in the industrial area for sensor data access. To achieve its objective, the architecture of GeoDADIS consists of: (i) a bottommost data acquisition layer that communicates with sensors, (ii) a middle kernel layer that provides general purpose functionality related to data management and system control and (iii) a topmost external interaction layer that enables the access from applications. The frameworks design does extensive use of the adapter (wrapper) design pattern to ease the incorporation of new data acquisition channels at the data acquisition layer and new data and remote control services in the external interaction layer. This makes GeoDADIS a very flexible and general purpose tool with broad application in many data monitoring domains.

Modular and Scalable Multi-interface Data Acquisition Architecture Design for Energy Monitoring in Fishing Vessels

Due to the increasing fuel price, the European fishing sector has been suffering a descendent trend since 1998. It is essential to find efficient solutions, through R&D, by the application of new technologies. This paper presents a portable, scalable and reusable data acquisition system for the categorization of energy consumption distribution in fishing vessels. Furthermore tools for processing, displaying and spreading the collected data have been developed. The resulting information will enable further analysis in order to draw energy savings and energy efficiency improvements.

Heterogeneous sensor data integration for crowdsensing applications

This paper describes a conceptual solution for heterogeneous sensor data integration in crowdsensing applications and one experimental implementation for a health monitoring system in an educational environment using a low cost hardware solution. Three kinds of protocols are integrated in this solution: HL7 for medical data, Observations and Measurements model for environmental data and BACnet for buildings monitoring. This last protocol has the particularity that manages sensoring and acting. A Common Data Model is described for the integration of three kinds of data and protocols, and a validation test application is described.

A sensor observation service based on OGC specifications for a meteorological SDI in Galicia

The MeteoSIX project, founded by the Galician regional government, aims at the development of a Spatial Data Infrastructure (SDI) and a new SDI based Geo web site to enable an integrated access to meteorological data for a wide variety of users with different skills. Such data has to be available through the internet using OGC and OpenNDAP standards. The present paper is focused on the design and implementation of a first prototype of a sensor observation web server whose interface is based on the OGC Sensor Observation Service (SOS).