Remote sensing data management and its use for classification and inference purposes is at the forefront of research tasks nowadays. There are, however, some inherent drawbacks and difficulties when dealing with, and understanding how satellite information is provided (particularly when referring to multiband/multispectral satellite platforms) and how different and disparate datasets related to soil content can be used and merged with this imagery.We present WALGREEN. The aim of this tool is to provide a secure environment to handle the whole process to use polygons or, geographical coordinates in tiff/geotiff images, have real-time access to images, save and get soil organic carbon real measurements, and generate datasets for machine learning training and inferential methods. We also aim to providing a framework to preprocess soil organic carbon information from different but accepted sources, like the Land Use/Cover Area frame statistical Survey database, so that even without real measurements, researchers may be able to start training different machine learning methodologies.

Multidimensional optical imaging systems for information processing and visualization technologies have numerous applications in fields such as manufacturing, medical sciences, entertainment, robotics, surveillance, and defense. Among different three-dimensional (3-D) imaging methods, integral imaging is a promising multiperspective sensing and display technique. Compared with other 3-D imaging techniques, integral imaging can capture a scene using an incoherent light source and generate real 3-D images for observation without any special viewing devices. This review paper describes passive multidimensional imaging systems combined with different integral imaging configurations. One example is the integral-imaging-based multidimensional optical sensing and imaging systems (MOSIS), which can be used for 3-D visualization, seeing through obscurations, material inspection, and object recognition from microscales to long range imaging. This system utilizes many degrees of freedom such as time and space multiplexing, depth information, polarimetric, temporal, photon flux and multispectral information based on integral imaging to record and reconstruct the multidimensionally integrated scene. Image fusion may be used to integrate the multidimensional images obtained by polarimetric sensors, multispectral cameras, and various multiplexing techniques. The multidimensional images contain substantially more information compared with two-dimensional (2-D) images or conventional 3-D images. In addition, we present recent progress and applications of 3-D integral imaging including human gesture recognition in the time domain, depth estimation, mid-wave-infrared photon counting, 3-D polarimetric imaging for object shape and material identification, dynamic integral imaging implemented with liquid-crystal devices, and 3-D endoscopy for healthcare applications.

Over the last years, three-dimensional (3-D) imaging has been applied to human action and gesture recognition, usually in the form of depth maps from RGB-D sensors. An alternative which has not been explored is 3-D integral imaging, aside from a recent preliminary study which shows that it can be an effective sensory modality with some advantages over the conventional monocular imaging. Since integral imaging has also been shown to be a powerful tool in other visual tasks (e.g., object reconstruction and recognition) under challenging conditions (e.g., low illumination, occlusions), and its passive long-range operation brings benefits over active close-range devices, a natural question is whether these advantages also hold for gesture recognition. Furthermore, occlusions are present in many real-world scenarios in gesture recognition, but it is an elusive problem which has scarcely been addressed. As far as we know, this letter analyzes for the first time the potential of integral imaging for gesture recognition under occlusions, by comparing it to monocular imaging and to RGB-D sensory data. Empirical results corroborates the benefits of 3-D integral imaging for gesture recognition, mainly under occlusions.

This paper presents an overview of research work and some novel strategies and results on using data fusion in 3-D imaging when using multiple information sources. We examine a variety of approaches and applications such as 3-D imaging integrated with polarimetric and multispectral imaging, low levels of photon flux for photon-counting 3-D imaging, and image fusion in both multiwavelength 3-D digital holography and 3-D integral imaging. Results demonstrate the benefits data fusion provides for different purposes, including visualization enhancement under different conditions, and 3-D reconstruction quality improvement.

In Spain, the new Medicine degrees of the European Higher Education Area (EHEA) have incorporated Information Technologies (IT) subjects to develop horizontal competences in their curriculum. Medicine Studies are costly for Higher Education (HE) institutions and, then, every cost-effective educational innovation is welcomed. There are different general approaches for designing and developing serious games in general, but specific target disciplines as Medicine (educational and professional training), lack of tailor-made methodologies, even in eliciting requirements under the Requirements Engineering (RE) framework, as a subfield of Software Engineering. In medical higher education, the main objective of using games is to engage learners in complex problem spaces that replicate real world situations, as it happens in hospital emergencies, surgery operations and so on, without the constraints and risks of the day to day practice (patient injuries, health complications and death). Therefore, the development of serious HE games is a complex, time consuming and costly process, especially for learning medical skills. In this Work in Progress, we address the problem of lacking suitable methodologies in the design and development processes of educational serious games for HE medical environments. Our proposal is a semi-formal and simple method to elicit and formalize the specific teaching needs identified in subjects or practices of Medicine degrees, in order to design and develop serious games based on sound educational principles and theories and on RE rules.

Scattering from a leaf responds differently at different wavelengths to changes in leaf properties such as pigment concentrations, chemical constituents, internal structure, and leaf-surface properties. Radiation scattered by leaves and exiting the vegetation canopy toward the sensor is affected by canopy structure. The concept of canopy spectral invariants is used to decompose multiangular hyperspectral Compact High Resolution Imaging Spectroradiometer–PROBA surface reflectances over agricultural crops during peak growth season into structural and optical components. The former, called the directional area scattering factor, is determined by the canopy geometrical properties and varies with crop type. The latter is a function of the leaf scattering properties and more directly related to the leaf interior. For dense crops, the decomposition technique does not require the use of canopy radiation models, prior knowledge, or ancillary information regarding the leaf scattering properties and thus provides a powerful means to remove canopy structural influences in hyperspectral remote sensing of leaf biochemical constituents. Our results also suggest that leaf-surface characteristics can increase canopy scattering spectra. This may decrease the ability to remotely sense leaf biochemistry.

A problem when working with optical satellite or airborne images is the need to compensate for changes in the illumination conditions at the time of acquisition. This is particularly critical when working with time series of data. Atmospheric correction strategies based on radiative transfer codes may provide a rigorous solution but it may not be the best solution for situations where a huge amount of hyperspectral images may need to be processed and computational time is a critical factor. The GMES (¿Global Monitoring for Environment and Security¿) initiative has promoted the creation of a new generation of satellites (the SENTINEL series) with ¿ultra-high resolution¿ and ¿superspectral imaging¿ capabilities. Therefore, there is an urgent need to quickly and reliably compensate for changes in the atmospheric transmittance and varying solar illumination conditions. In this paper three different forms of affine transformation models (general, particular and diagonal) are considered as candidates for rapid compensation of illumination variations. They are tested on a series of simulated multispectral images of Top-Of-Atmosphere (TOA) radiance, where the surface is a synthetic scene of a test site in Spain called Barrax, where reference data for validation is available. The results indicate that in 2 of the more moderate Sun positions, for all the Visibilities tested, the particular affine method is better than the other 2. The results also indicate that the proposed methodology is satisfactory for practical normalization of varying illumination and atmospheric conditions in remotely sensed images required for operational or time critical applications.

The octahedral group is one of the finite subgroups of the rotation group in 3-D Euclidean space and a symmetry group of the cubic grid. Compression and filtering of 3-D volumes are given as application examples of its representation theory. We give an overview over the finite subgroups of the 3-D rotation group and their classification. We summarize properties of the octahedral group and basic results from its representation theory. Wide-sense stationary processes are processes with group theoretical symmetries whose principal components are closely related to the representation theory of their symmetry group. Linear filter systems are defined as projection operators and symmetry-based filter systems are generalizations of the Fourier transforms. The algorithms are implemented in Maple/Matlab functions and worksheets. In the experimental part, we use two publicly available MRI volumes. It is shown that the assumption of wide-sense stationarity is realistic and the true principal components of the correlation matrix are very well approximated by the group theoretically predicted structure. We illustrate the nature of the different types of filter systems, their invariance and transformation properties. Finally, we show how thresholding in the transform domain can be used in 3-D signal processing.

The non-negativity of color signals implies that they span a conical space with a hyperbolic geometry. We use perspective projections to separate intensity from chromaticity, and for 3-D color descriptors the chromatic properties are represented by points on the unit disk. Descriptors derived from the same object point but under different imaging conditions can be joined by a hyperbolic geodesic. The properties of this model are investigated using multichannel images of natural scenes and black body illuminants of different temperatures. We show, over a series of static scenes with different illuminants, how illumination changes influence the hyperbolic distances and the geodesics. Descriptors derived from conventional RGB images are also addressed.