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Sensors provide robotic systems with the information required to perceive the changes that happen in unstructured environments and modify their actions accordingly. The robotic controllers which process and analyze this sensory information are usually based on three types of sensors (visual, force/torque and tactile) which identify the most widespread robotic control strategies: visual servoing control, force control and tactile control. This paper presents a detailed review on the sensor architectures, algorithmic techniques and applications which have been developed by Spanish researchers in order to implement these mono-sensor and multi-sensor controllers which combine several sensors. [ABSTRACT FROM AUTHOR]

This paper presents an assessment of the performance of a hybrid method that allows a simultaneous retrieval of land-surface temperature (LST) and emissivity (LSE) from remotely-sensed data. The proposed method is based on a synergistic usage of the split-window (SW) algorithm and the two-temperature method (TTM) and combines the advantages of both procedures while mitigating their drawbacks. The method was implemented for thermal channels 76 (10.56 µm) and 78 (11.72 µm) of the Airborne Hyperspectral Scanner (AHS), which was flown over the Barrax test site (Albacete, Spain) in the second week of July 2005, within the framework of the Sentinel-2 and Fluorescence Experiment (SEN2FLEX) field campaign. A set of radiometric measurements was performed in the thermal infrared region in coincidence with aircraft overpasses for different surface types, e.g. bare soil, water body, corn, wheat, grass. The hybrid method was tested and compared with a standard SW algorithm and the results obtained show that the hybrid method is able to provide better estimates of LST, with values of bias (RMSE) of the order of 0.8 K (1.9 K), i.e. about one third (one half) of the corresponding values of 2.7 K (3.4 K) that were obtained for bias (RMSE) when using the SW algorithm. These figures provide a sound indication that the developed hybrid method is particularly useful for surface and atmospheric conditions where SW algorithms cannot be accurately applied. [ABSTRACT FROM AUTHOR]

Abstract: We present in this paper the Generic RAdiative traNsfer AnD non-LTE population Algorithm (GRANADA). This model is able to compute non-LTE populations for vibrational, rotational, spin (i.e., NO and OH), and electronic (i.e., O2) states in a given planetary atmosphere. The model is very flexible and can be used for computing very accurate non-LTE populations or for calculating reasonably accurate but at high speed non-LTE populations in order to implement it into non-LTE remote sensing retrievals. We describe the model in detail and present an update of the non-LTE collisional processes and their rate coefficients for the most important molecules in Earth''s atmosphere. In addition, we have applied the model to the most important atmospheric infrared emitters including 13 species (H2O, CO2, O3, N2O, CO, CH4, O2, NO, NO2, HNO3, OH, N2, and HCN) and 460 excited vibrational or electronic energy levels. Non-LTE populations for all these energy levels have been calculated for 48 reference atmospheres expanding from the surface up to 200km, including seasonal (January, April, July and October), latitudinal (75°S, 45°S, 10°S, 10°N, 45°N, 75°N) and diurnal (day and night) coverages. The effects of the most recent updates of the non-LTE collisional parameters on the non-LTE populations are briefly described. This climatology is available online to the community and it can be used for estimating non-LTE effects at specific conditions and for testing and validation studies. [Copyright &y& Elsevier]

Abstract: Land cover monitoring using remotely sensed data requires robust classification methods which allow for the accurate mapping of complex land cover and land use categories. Random forest (RF) is a powerful machine learning classifier that is relatively unknown in land remote sensing and has not been evaluated thoroughly by the remote sensing community compared to more conventional pattern recognition techniques. Key advantages of RF include: their non-parametric nature; high classification accuracy; and capability to determine variable importance. However, the split rules for classification are unknown, therefore RF can be considered to be black box type classifier. RF provides an algorithm for estimating missing values; and flexibility to perform several types of data analysis, including regression, classification, survival analysis, and unsupervised learning. In this paper, the performance of the RF classifier for land cover classification of a complex area is explored. Evaluation was based on several criteria: mapping accuracy, sensitivity to data set size and noise. Landsat-5 Thematic Mapper data captured in European spring and summer were used with auxiliary variables derived from a digital terrain model to classify 14 different land categories in the south of Spain. Results show that the RF algorithm yields accurate land cover classifications, with 92% overall accuracy and a Kappa index of 0.92. RF is robust to training data reduction and noise because significant differences in kappa values were only observed for data reduction and noise addition values greater than 50 and 20%, respectively. Additionally, variables that RF identified as most important for classifying land cover coincided with expectations. A McNemar test indicates an overall better performance of the random forest model over a single decision tree at the 0.00001 significance level. [Copyright &y& Elsevier]

Abstract: The validation of coarse satellite-derived products from field measurements generally relies on up-scaling the field data to the corresponding satellite products. This question is commonly addressed through the generation of a reference high-resolution map of an area covering several moderate resolution pixels. This paper describes a method by which reference leaf area index (LAI) maps can be generated from ground-truth LAI measurements. The method is based on a multivariate ordinary least squares (OLS) algorithm which uses an iteratively re-weighted least squares (IRLS) algorithm. It provides an empirical relationship (i.e. a transfer function) between in situ measurements and concomitant radiance values from high-resolution HRVIR2/SPOT4 imagery. The band combination composed of green (G), red (R) and near infrared (NIR) proved to be appropriate for LAI prediction, with a cross-validation error lower than 0.7. The convex hull technique has been proposed to assess the extrapolation error of the up-scaling model. Although the model showed to be sensitive to pixels outside the convex hull, the error associated with extrapolated pixels was moderate (i.e. root mean square error (RMSE)≈0.3). A close agreement was found between LAI maps derived from different field based datasets despite the notable differences in the sampling constraints imposed by the use of different instruments. It confirms the validity of a stratified random sampling strategy and suggests the suitability of using around 50 elementary sampling units (ESUs) to characterize the study area. A sensitivity analysis has confirmed that this method is suited to capture the variability across the site extent and, at the same time, minimize field efforts. Knowledge gained from this work provides guidance for an efficient sampling strategy of LAI in a cropland area. [Copyright &y& Elsevier]