Semantic Approach in Image Change Detection

International audienceChange detection is a main issue in various domains, and especially for remote sensing purposes. Indeed, plethora of geospatial images are available and can be used to update geographical databases. In this paper, we propose a classification-based method to detect changes between a database and a more recent image. It is based both on an efficient training point selection and a hierarchical decision process. This allows to take into account the intrinsic heterogeneity of the objects and themes composing a database while limiting false detection rates. The reliability of the designed framework method is first assessed on simulated data, and then successfully applied on very high resolution satellite images and two land-cover databases

Arc-welding spectroscopic monitoring based on feature selection and neural networks

A new spectral processing technique designed for application in the on-line detection and classification of arc-welding defects is presented in this paper. A noninvasive fiber sensor embedded within a TIG torch collects the plasma radiation originated during the welding process. The spectral information is then processed in two consecutive stages. A compression algorithm is first applied to the data, allowing real-time analysis. The selected spectral bands are then used to feed a classification algorithm, which will be demonstrated to provide an efficient weld defect detection and classification. The results obtained with the proposed technique are compared to a similar processing scheme presented in previous works, giving rise to an improvement in the performance of the monitoring system

Defect Detection in Arc-Welding Processes by Means of the Line-to-Continuum Method and Feature Selection

Plasma optical spectroscopy is widely employed in on-line welding diagnostics. The determination of the plasma electron temperature, which is typically selected as the output monitoring parameter, implies the identification of the atomic emission lines. As a consequence, additional processing stages are required with a direct impact on the real time performance of the technique. The line-to-continuum method is a feasible alternative spectroscopic approach and it is particularly interesting in terms of its computational efficiency. However, the monitoring signal highly depends on the chosen emission line. In this paper, a feature selection methodology is proposed to solve the uncertainty regarding the selection of the optimum spectral band, which allows the employment of the line-to-continuum method for on-line welding diagnostics. Field test results have been conducted to demonstrate the feasibility of the solution

Fomento del razonamiento crítico mediante la evaluación cruzada: estudio de casos en asignaturas de ciencias

[EN] The peer-reviewing process fosters the participation of students in class by means of the evaluation of the activities carried out by their colleagues. In order for this procedure to be successful, it is necessary to introduce the activity and motivate it properly, as well as to define detailed and specific evaluation rubrics which gather all the learning goals. This study summarizes and analyses several peer-reviewing application cases performed during Sciences courses with the aim of detecting common patterns and differences between them. After comparing the grades obtained by following this process and reviewing several surveys about it, it can be concluded that, although some marginal discrepancies exist between the scores given by the professor and the students, their involvement in the evaluation process has a positive impact in their perception of the learning level and the adequacy of the evaluation system. In this way, the students are able to identify by themselves the strong and weak aspects of their work, which results also in an increase of their critical thinking. In addition, the final grade does not depend only on the criterion of the professor, but also on the interpretation of several previously established criteria done by the participants in the activity.[ES] El procedimiento de evaluación cruzada fomenta la participación en clase de los estudiantes mediante la valoración de las actividades llevadas a cabo por sus compañeros. Para que sea útil, es necesario introducir la actividad y motivarla adecuadamente, así como definir rúbricas detalladas y concretas que recojan todos los objetivos de aprendizaje. Este estudio recopila diferentes casos de aplicación de evaluación cruzada en asignaturas de ciencias, en donde se analizan las particularidades de cada caso con la finalidad de analizar patrones comunes y diferencias, así como plantear mejoras en su aplicación futura. A través de comparativas de notas y encuestas al alumnado se demuestra que, aun existiendo ligeras discrepancias entre las calificaciones otorgadas por los alumnos y el profesor, el nivel de implicación del alumno en el proceso evaluador redunda positivamente en su percepción del nivel aprendizaje y la adecuación del sistema de evaluación. Así, el alumno es capaz de identificar por sí mismo los puntos fuertes y débiles de su trabajo, redundando en un mayor espíritu crítico. Por otra parte, la calificación no depende solo del criterio de una persona, sino de la interpretación de varias personas sobre unos criterios comunes previamente establecidos.*Este trabajo ha sido realizado en el marco del proyecto docente UV-SFPIE PID-1640839: “Docencia y evaluación a distancia: uso de herramientas propias de la UV y externas para mejorar la metodología docente en línea e híbrida en el área de ciencias”.Ruescas, A.; Fernandez-Morán, R.; Moreno-Llácer, M.; Fernández-Torres, M.; Amorós-López, J.; Adsuara, J.; Esperante, D.... (2022). Fomento del razonamiento crítico mediante la evaluación cruzada: estudio de casos en asignaturas de ciencias. Editorial Universitat Politècnica de València. 314-326. https://doi.org/10.4995/INRED2022.2022.1587831432

Cloud Mask Intercomparison eXercise (CMIX): An evaluation of cloud masking algorithms for Landsat 8 and Sentinel-2

Cloud cover is a major limiting factor in exploiting time-series data acquired by optical spaceborne remote sensing sensors. Multiple methods have been developed to address the problem of cloud detection in satellite imagery and a number of cloud masking algorithms have been developed for optical sensors but very few studies have carried out quantitative intercomparison of state-of-the-art methods in this domain. This paper summarizes results of the first Cloud Masking Intercomparison eXercise (CMIX) conducted within the Committee Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV). CEOS is the forum for space agency coordination and cooperation on Earth observations, with activities organized under working groups. CMIX, as one such activity, is an international collaborative effort aimed at intercomparing cloud detection algorithms for moderate-spatial resolution (10–30 m) spaceborne optical sensors. The focus of CMIX is on open and free imagery acquired by the Landsat 8 (NASA/USGS) and Sentinel-2 (ESA) missions. Ten algorithms developed by nine teams from fourteen different organizations representing universities, research centers and industry, as well as space agencies (CNES, ESA, DLR, and NASA), are evaluated within the CMIX. Those algorithms vary in their approach and concepts utilized which were based on various spectral properties, spatial and temporal features, as well as machine learning methods. Algorithm outputs are evaluated against existing reference cloud mask datasets. Those datasets vary in sampling methods, geographical distribution, sample unit (points, polygons, full image labels), and generation approaches (experts, machine learning, sky images). Overall, the performance of algorithms varied depending on the reference dataset, which can be attributed to differences in how the reference datasets were produced. The algorithms were in good agreement for thick cloud detection, which were opaque and had lower uncertainties in their identification, in contrast to thin/semi-transparent clouds detection. Not only did CMIX allow identification of strengths and weaknesses of existing algorithms and potential areas of improvements, but also the problems associated with the existing reference datasets. The paper concludes with recommendations on generating new reference datasets, metrics, and an analysis framework to be further exploited and additional input datasets to be considered by future CMIX activities

Coupled retrieval of aerosol optical thickness, columnar water vapor and surface reflectance maps from ENVISAT/MERIS data over land

An algorithm for the derivation of atmospheric parameters and surface reflectance data from MEdium Resolution Imaging Specrometer Instrument (MERIS) on board ENVIronmental SATellite (ENVISAT) images has been developed. Geo-rectified aerosol optical thickness (AOT), columnar water vapor (CWV) and spectral surface reflectance maps are generated from MERIS Level-1b data over land. The algorithm has been implemented so that AOT, CWV and reflectance products are provided on an operational manner, making no use of ancillary parameters apart from those attached to MERIS products. For this reason, it has been named Self-Contained Atmospheric Parameters Estimation from MERIS data (SCAPE-M). The fundamental basis of the algorithm and applicable error figures are presented in the first part of this paper. In particular, errors of ± 0.03, ± 4% and ± 8% have been estimated for AOT, CWV and surface reflectance retrievals, respectively, by means of a sensitivity analysis based on a synthetic data set simulated under a usual MERIS scene configuration over land targets. The assumption of a fixed aerosol model, the coarse spatial resolution of the AOT product and the neglection of surface reflectance directional effects were also identified as limitations of SCAPE-M. Validation results are detailed in the second part of the paper. Comparison of SCAPE-M AOT retrievals with data from AErosol RObotic NETwork (AERONET) stations showed an average Root Mean Square Error (RMSE) of 0.05, and an average correlation coefficient R2 of about 0.7-0.8. R2 values grew up to more than 0.9 in the case of CWV after comparison with the same stations. A good correlation is also found with the MERIS Level-2 ESA CWV product. Retrieved surface reflectance maps have been successfully compared with reflectance data derived from the Compact High Resolution Imaging Spectrometer (CHRIS) on board the PRoject for On-Board Autonomy (PROBA) in the first place. Reflectance retrievals have also been compared with reflectance data derived from MERIS images by the Bremen AErosol Retrieval (BAER) method. A good correlation in the red and near-infrared bands was found, although a considerably higher proportion of pixels was successfully processed by SCAPE-M. © 2008 Elsevier Inc. All rights reserved

An IP Core and GUI for Implementing Multilayer Perceptron with a Fuzzy Activation Function on Configurable Logic Devices

This paper describes the development of an Intellectual Property (IP) core in VHDL able to implement a Multilayer Perceptron (MLP) artificial neural network (ANN) topology with up to 2 hidden layers, 128 neurons, and 31 inputs per neuron. Neural network models are usually developed by using programming languages, such as Matlab®. However, their implementation in configurable logic hardware requires the use of some other tools and hardware description languages, such as as VHDL. For easy migration, a Matlab Graphical User Interface (GUI) to automatically translate the ANN architecture to VHDL code has been developed. In addition, the use of an activation function based on fuzzy logic for the implementation of the MLP neural network simplifies the logic and improves the results. The environment was tested using a typical prediction problem, the Mackey-Glass series, where several ANN topologies were generated, tested and implemented in an FPGA. Results show the excellent agreement between the results provided by the software model and the hardware implementation