A Vegetation Index (VI) is easily accessible and is associated with a wide spatial and temporal availability. However, are these datasets really useful? One of the main difficulties related to their use, such as MODIS time-series, is the potentially high number of pixels with low radiometric quality. In this work, a Quality Assurance analysis was performed using four MODIS VI scenes from 2011, based on the flags Pixel Reliability and Quality Detailed. The considered dataset covers two different areas of Portugal and two distinct species. Two VI products were analyzed: Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). The EVI presented generally better results than the NDVI. Additionally, both NDVI and EVI indexes presented a seasonal dependence with better quality in the dry season, and a geographical/land use influence supported by better quality observed in the south area. In conclusion, quality indexes should be considered in VI applications.

In the fetus, the hypoxic stimulation inhibits respiratory movements, while after the birth, it stimulates breathing. Sudden Infant Death Syndrome (SIDS) may result from a resurgence of the prenatal inhibiting circuit (Leiter, 2007). This study aims to compare the entropic evolutionary profile of the "Recurrent Elevated PatternA" (REPA) of fetal heart rate, to the one described in SIDS by Pincus during quiet sleep, the postnatal corresponding state of PatternA. Using an unselected 3000 fetal tracings database, a computer assisted visual analysis selected the 16 most typical REPA, compared to the 22 most stable PatternA (MSPA). The entropic study enabled to extract from each group a type of description corresponding to Pincus criteria: instable entropy with remarkable minima for REPA, and single centered regular entropy peak for MSPA. If confirmed, our findings may lead to a better evaluation of the fetal status and to a SIDS preventive protocol via a trans birth survey.

Automatic image registration (AIR) is still a present challenge for the remote sensing community. Although a wide variety of AIR methods have been proposed in the last few years, there are several drawbacks which avoid their common use in practice. The recently proposed scale invariant feature transform (SIFT) approach has already revealed to be a powerful tool for the obtention of tie points in general image processing tasks, but it has a limited performance when directly applied to remote sensing images. In this paper, a new AIR method is proposed, based on the combination of image segmentation and SIFT, complemented by a robust procedure of outlier removal. This combination allows for an accurate obtention of tie points for a pair of remote sensing images, being a powerful scheme for AIR. Both synthetic and real data have been considered in this work for the evaluation of the proposed methodology, comprising medium and high spatial resolution images, and single-band, multispectral, and hyperspectral images. A set of measures which allow for an objective evaluation of the geometric correction process quality has been used. The proposed methodology allows for a fully automatic registration of pairs of remote sensing images, leading to a subpixel accuracy for the whole considered data set. Furthermore, it is able to account for differences in spectral content, rotation, scale, translation, different viewpoint, and change in illumination.

Automatic image registration is still an actual challenge in several fields. Although several methods for automatic image registration have been proposed in the last few years, it is still far from a broad use in several applications, such as in remote sensing. In this paper, a method for automatic image registration through histogram-based image segmentation (HAIRIS) is proposed. This new approach mainly consists in combining several segmentations of the pair of images to be registered, according to a relaxation parameter on the histogram modes delineation (which itself is a new approach), followed by a consistent characterization of the extracted objects--through the objects area, ratio between the axis of the adjust ellipse, perimeter and fractal dimension--and a robust statistical based procedure for objects matching. The application of the proposed methodology is illustrated to simulated rotation and translation. The first dataset consists in a photograph and a rotated and shifted version of the same photograph, with different levels of added noise. It was also applied to a pair of satellite images with different spectral content and simulated translation, and to real remote sensing examples comprising different viewing angles, different acquisition dates and different sensors. An accuracy below 1° for rotation and at the subpixel level for translation were obtained, for the most part of the considered situations. HAIRIS allows for the registration of pairs of images (multitemporal and multisensor) with differences in rotation and translation, with small differences in the spectral content, leading to a subpixel accuracy.

The geometric correction process is a crucial step in remote sensing applications. This process is frequently manually performed-which is a laborious task in many situations-as automatic image registration methods are still far from being broadly applied. One of the reasons that justify the absence of a broad application of automatic image registration methods is the lack of measures for an objective and automated analysis of the image registration process quality. The root mean square (RMS) of the residuals is the only quantitative evaluation which is generally used in this process, with the final validation of the geometric correction process being a qualitative analysis. Therefore, in both ldquohumanrdquo and automatic image registration processes, an objective evaluation of its quality is required. In this letter, we propose several measures for an objective evaluation of the geometric correction process, as a complement to the traditional RMS of the residuals and visual inspection. Two scenarios of control point distribution and the most common residual distributions were considered. With the proposed measures, we intend to cover the most common qualitative analysis aspects. This has particular importance under the scope of automatic image registration methods, where an automatic evaluation of the results is also required.

Spatial and temporal variation of river plumes can be studied by remote sensing. The main objectives of this letter were to model the Douro River Plume (DRP) size based on image segmentation techniques and to relate it to different parameters such as water volume, last available plume, tide height, and wind speed. Twenty-one MEdium Resolution Imaging Spectrometer (MERIS) scenes of the study area were considered, covering 20 months from 2003 to 2005. Two different segmentation techniques were applied (watershed and region based) to the MERIS scenes in order to estimate the DRP size. Two models were developed in order to relate the DRP size with the water volume, last available plume, tide height, and wind speed. In the first model, the DRP size was modeled using the discharged water volume as input. In the second model, the DRP size was retrieved as a function of the water volume, last available plume, tide height, and wind speed. The last available plume-a factor of crucial importance-was weighted using a forgetting factor, function of the time difference between two consecutive MERIS scenes. A significant correlation was found between DRP dimension and water volume (r = 0.66) during wet season (i.e., excluding dry summer period), because the plume derived from MERIS imagery in the region under study represents river Douro plume only when the river flow exceeds a certain threshold. Despite some particular points, the second model was able to model the plume size obtained through image segmentation, with a mean percentage variation of 34.8% for region-based segmentation method. Segmentation of MERIS data have been shown to be a valid method for modeling the DRP size. Furthermore, DRP size was found to be proportional to water volume, excluding the summer period.

In this paper, we present different methodologies to estimate the total suspended matter (TSM) concentration in a particular area of the Portuguese coast, from remotely sensed multispectral data, based on single-band models, multiple regression, and artificial neural networks (ANNs). Simulations on different beaches of the study area were performed to determine a relationship between the TSM concentration and the spectral response of the seawater. Based on thein situmeasurements, empirical models were established in order to relate the seawater reflectance with the TSM concentration for TERRA/ASTER, SPOT HRVIR, and Landsat/TM. Seven images of these three sensors were calibrated and atmospherically and geometrically corrected. Single-band models, multiple regression, and ANNs were applied to the visible and near-infrared (NIR) bands of these sensors in order to estimate the TSM concentration. Statistical analysis using correlation coefficients and error estimation was employed, aiming to evaluate the most accurate methodology. The chosen methodology was further applied to the seven processed images. The analysis of the root-mean-square errors achieved by both the linear and nonlinear models supports the hypothesis that the relationship between the seawater reflectance and TSM concentration is clearly nonlinear. The ANNs have been shown to be useful in estimating the TSM concentration from reflectance of visible and NIR bands of ASTER, HRVIR, and TM sensors, with better results for ASTER and HRVIR sensors. Maps of TSM concentration were produced for all satellite images processed.