María Concepcion Alonso

Dempster–Shafer Theory in geographic information systems: A survey

Abstract Since the information used in a Geographic Information System has a certain degree of uncertainly, in general classical mathematics models should not be applied to solve geographical problems computationally. Therefore, probabilistic or fuzzy-related methods should be considered, in order to model the behaviour of real problems that have to be solved by or with a Geographic Information System. In this paper, a review of the application of Dempster–Shafer Theory of Evidence—also called “belief functions”—in relation to Geographic Information System is given. The review will focus on classification as a way of fusing information in a Geographic Information System. Information fusion, for classification, represents the first step in the abstraction of information and a means of data mining, and both the advantages and limitations of the technique of the Theory of Evidence in comparison to other techniques are analysed.

A projection pursuit algorithm for anomaly detection in hyperspectral imagery

The main goal of this paper is to propose an innovative technique for anomaly detection in hyperspectral imageries. This technique allows anomalies to be identified whose signatures are spectrally distinct from their surroundings, without any a priori knowledge of the target spectral signature. It is based on an one-dimensional projection pursuit with the Legendre index as the measure of interest. The index optimization is performed with a simulated annealing over a simplex in order to bypass local optima which could be sub-optimal in certain cases. It is argued that the proposed technique could be considered as seeking a projection to depart from the normal distribution, and unfolding the outliers as a consequence. The algorithm is tested with AHS and HYDICE hyperspectral imageries, where the results show the benefits of the approach in detecting a great variety of objects whose spectral signatures have sufficient deviation from the background. The technique proves to be automatic in the sense that there is no need for parameter tuning, giving meaningful results in all cases. Even objects of sub-pixel size, which cannot be made out by the human naked eye in the original image, can be detected as anomalies. Furthermore, a comparison between the proposed approach and the popular RX technique is given. The former outperforms the latter demonstrating its ability to reduce the proportion of false alarms.

Sex differences in fingerprint ridge density in the Mataco-Mataguayo population

Ridge density (RD), the number of digital ridges per unit area, varies according to sex, age, and population origin. The main objective of this study was to determine the extent of sexual dimorphism in RD and to set the age at which it appears, in an Amerindian sample from the Mataco-Mataguayo population. The sample studied for this research consisted of 99 males and 110 females, between 6 and 25 years old, which amounts to a total of 2090 fingerprints. Ridge count was carried out on distal radial and distal ulnar and on proximal regions of each finger to explore the RD patterns in order to identify similarities and differences among samples, areas, age groups, and sexes. RD decreased with age and, at all ages, RD was higher on the distal (radial and ulnar) areas, followed by the proximal sides. Females were found to have higher RD than males when older than 12 years, but not when younger. In the radial area, the Mataco-Mataguayo population, in both sexes, presented the RD similar to Spanish samples, but higher than all other populations analysed to date using this method. Variations in RD in the Amerindian population based on sex, age, and topology were confirmed in this work, and it is postulated that these variations are due to developmental differences among individuals and populations. A comparison between the Mataco-Mataguayo and Spanish populations is presented.

Automatic Detection and Classification of Pole-Like Objects in Urban Point Cloud Data Using an Anomaly Detection Algorithm

Detecting and modeling urban furniture are of particular interest for urban management and the development of autonomous driving systems. This paper presents a novel method for detecting and classifying vertical urban objects and trees from unstructured three-dimensional mobile laser scanner (MLS) or terrestrial laser scanner (TLS) point cloud data. The method includes an automatic initial segmentation to remove the parts of the original cloud that are not of interest for detecting vertical objects, by means of a geometric index based on features of the point cloud. Vertical object detection is carried out through the Reed and Xiaoli (RX) anomaly detection algorithm applied to a pillar structure in which the point cloud was previously organized. A clustering algorithm is then used to classify the detected vertical elements as man-made poles or trees. The effectiveness of the proposed method was tested in two point clouds from heterogeneous street scenarios and measured by two different sensors. The results for the two test sites achieved detection rates higher than 96%; the classification accuracy was around 95%, and the completion quality of both procedures was 90%. Non-detected poles come from occlusions in the point cloud and low-height traffic signs; most misclassifications occurred in man-made poles adjacent to trees.

Change detection of buildings from satellite imagery and lidar data

Geospatial objects change over time and this necessitates periodic updating of the cartography that represents them. Currently, this updating is done manually, by interpreting aerial photographs, but this is an expensive and time-consuming process. While several kinds of geospatial objects are recognized, this article focuses on buildings. Specifically, we propose a novel automatic approach for detecting buildings that uses satellite imagery and laser scanner data as a tool for updating buildings for a vector geospatial database. We apply the support vector machine (SVM) classification algorithm to a joint satellite and laser data set for the extraction of buildings. SVM training is automatically carried out from the vector geospatial database. For visualization purposes, the changes are presented using a variation of the traffic-light map. The different colours assist human operators in performing the final cartographic updating. Most of the important changes were detected by the proposed method. The method not only detects changes, but also identifies inaccuracies in the cartography of the vector database. Small houses and low buildings surrounded by high trees present significant problems with regard to automatic detection compared to large houses and taller buildings. In addition to visual evaluation, this study was checked for completeness and correctness using numerical evaluation and receiver operating characteristic curves. The high values obtained for these parameters confirmed the efficacy of the method.

Topological Variability of Fingerprint Ridge Density in a Sub‐Saharan Population Sample for Application in Personal Identification

Variability in ridge density in a sub‐Saharan population sample was studied by counting ridges in three fingerprint areas (two distal regions, radial and ulnar, and one proximal region) on the epidermal surface of the distal phalanx. Study material was obtained from the fingerprint impressions of 100 male sub‐Saharan subjects aged between 18‐ and 48‐years old. The results were compared with those obtained from a Spanish population sample. Sub‐Saharan males presented lower ridge density than Spanish males in the distal regions (radial and ulnar) of all fingers, whereas differences in the proximal region were only observed on some fingers. Using the differences observed between these populations, the likelihood ratio for inferring membership of one of the populations from a fingerprint of unknown origin was calculated; therefore, a ridge density of 14 or less for both areas (ulnar and radial), support an origin sub‐Saharan versus Spanish population.

Classification of Multispectral High-Resolution Satellite Imagery Using LIDAR Elevation Data

This paper studies the influence of airborne LIDAR elevation data on the classification of multispectral SPOT5 imagery over a semi-urban area; to do this, multispectral and LIDAR elevation data are integrated in a single imagery file composed of independent multiple bands. The Support Vector Machine is used to classify the imagery. A scheme of five classes was chosen; ground truth samples were then collected in two sets, one for training the classifier and the other for checking its quality after classification. The results show that the integration of LIDAR elevation data improves the classification of multispectral bands; the assessment and comparison of the classification results have been carried out using complete confusion matrices. Improvements are evident in classes with similar spectral characteristics but for which altitude is a relevant discrimination factor. An overall improvement of 28.3% was obtained, when LIDAR was included.

Semi-Automatic Detection of Swimming Pools from Aerial High-Resolution Images and LIDAR Data

Bodies of water, particularly swimming pools, are land covers of high interest. Their maintenance involves energy costs that authorities must take into consideration. In addition, swimming pools are important water sources for firefighting. However, they also provide a habitat for mosquitoes to breed, potentially posing a serious health threat of mosquito-borne disease. This paper presents a novel semi-automatic method of detecting swimming pools in urban environments from aerial images and LIDAR data. A new index for detecting swimming pools is presented (Normalized Difference Swimming Pools Index) that is combined with three other decision indices using the Dempster–Shafer theory to determine the locations of swimming pools. The proposed method was tested in an urban area of the city of Alcala de Henares in Madrid, Spain. The method detected all existing swimming pools in the studied area with an overall accuracy of 99.86%, similar to the results obtained by support vector machines (SVM) supervised classification.

A Spatial Contextual Postclassification Method for Preserving Linear Objects in Multispectral Imagery

Classification of remote sensing multispectral data is important for segmenting images and thematic mapping and is generally the first step in feature extraction. Per-pixel classification, based on spectral information alone, generally produces noisy classification results. The introduction of spatial information has been shown to be beneficial in removing most of this noise. Probabilistic label relaxation (PLR) has proved to be advantageous using second-order statistics; here, we present a modified contextual probabilistic relaxation method based on imposing directional information in the joint probability with third-order statistics. The proposed method was tested in synthetic images and real images; the results are compared with a “Majority” algorithm and the classical PLR method. The proposed third-order method gives the best results, both visually and numerically.

Mortalidad infantil por malformaciones congénitas y condición socioeconómica: el caso de la Argentina

OBJETIVO: Relacionar la tasa de mortalidad infantil por malformaciones congenitas (TMIMC) y el porcentaje de muertes por malformaciones congenitas (%MMC) con las caracteristicas sociodemograficas y economicas en la Argentina. METODOS: La poblacion estudiada de la Argentina reside en 511 departamentos de 23 provincias, agrupadas en cinco regiones geograficas (Noroeste, Noreste, Centro, Cuyo y Patagonia). Las variables analizadas fueron la TMLMC y el %MMC calculados a partir de los nacimientos y las defunciones del quinquenio 2002-2006. Ademas, se utilizaron 21 variables del Censo de Poblacion y Vivienda del 2001 (Instituto Nacional de Estadistica y Censos de Argentina) para construir el Indicador Sociodemografico y Economico (ISDE) mediante el analisis de componentes principales. Se realizaron pruebas de comparacion para valorar si aparecian diferencias significativas entre las distintas regiones y las correlaciones entre indicadores, y de estos con la latitud y longitud departamental. RESULTADOS: La TMIMC no presento correlacion significativa con el ISDE ni con las coor denadas geograficas. El %MMC y el ISDE presentaron una correlacion positiva significativa (P < 0,05) en todos los niveles de organizacion politica. El ISDE explico 41% de la variacion del %MMC. CONCLUSIONES: La TMIMC no se asocio significativamente con la marcada heterogeneidad socioeconomica del pais; los valores mas elevados del %MMC, en cambio, se observaron en las poblaciones del centro y sur del pais. Dada la relacion entre el %MMC y el desarrollo socioeconomico poblacional se sugiere utilizar este indicador como una aproximacion (proxy) de bienestar y calidad de vida.