Assessment of "mobile mapping system": A GIS-based support methodology

In this article, the authors examine the current status of different elements that integrate the landscape of the municipality of Olias del Rey in Toledo (Spain). A methodology for the study of rural roads, activity farming and local hunting management. We used Geographic Information Technologies (GIT) in order to optimize spatial information including the design of a Geographic Information System (GIS). In the acquisition of field data we have used vehicle "mobile mapping" instrumentation equipped with GNSS, LiDAR, digital cameras and odometer. The main objective is the integration of geoinformation and geovisualization of the information to provide a fundamental tool for rural planning and management.

Detección de patrones y anomalías espectrales del terreno mediante espectrometría de imagen de alta resolución : reconocimiento, optimización y evaluación multiescenario

El objeto de esta Tesis doctoral es el desarrollo de una metodologia para la deteccion automatica de anomalias a partir de datos hiperespectrales o espectrometria de imagen, y su cartografiado bajo diferentes condiciones tipologicas de superficie y terreno. La tecnologia hiperespectral o espectrometria de imagen ofrece la posibilidad potencial de caracterizar con precision el estado de los materiales que conforman las diversas superficies en base a su respuesta espectral. Este estado suele ser variable, mientras que las observaciones se producen en un numero limitado y para determinadas condiciones de iluminacion. Al aumentar el numero de bandas espectrales aumenta tambien el numero de muestras necesarias para definir espectralmente las clases en lo que se conoce como Maldicion de la Dimensionalidad o Efecto Hughes (Bellman, 1957), muestras habitualmente no disponibles y costosas de obtener, no hay mas que pensar en lo que ello implica en la Exploracion Planetaria. Bajo la definicion de anomalia en su sentido espectral como la respuesta significativamente diferente de un pixel de imagen respecto de su entorno, el objeto central abordado en la Tesis estriba primero en como reducir la dimensionalidad de la informacion en los datos hiperespectrales, discriminando la mas significativa para la deteccion de respuestas anomalas, y segundo, en establecer la relacion entre anomalias espectrales detectadas y lo que hemos denominado anomalias informacionales, es decir, anomalias que aportan algun tipo de informacion real de las superficies o materiales que las producen. En la deteccion de respuestas anomalas se asume un no conocimiento previo de los objetivos, de tal manera que los pixeles se separan automaticamente en funcion de su informacion espectral significativamente diferenciada respecto de un fondo que se estima, bien de manera global para toda la escena, bien localmente por segmentacion de la imagen. La metodologia desarrollada se ha centrado en la implicacion de la definicion estadistica del fondo espectral, proponiendo un nuevo enfoque que permite discriminar anomalias respecto fondos segmentados en diferentes grupos de longitudes de onda del espectro, explotando la potencialidad de separacion entre el espectro electromagnetico reflectivo y emisivo. Se ha estudiado la eficiencia de los principales algoritmos de deteccion de anomalias, contrastando los resultados del algoritmo RX (Reed and Xiaoli, 1990) adoptado como estandar por la comunidad cientifica, con el metodo UTD (Uniform Targets Detector), su variante RXD-UTD, metodos basados en subespacios SSRX (Subspace RX) y metodo basados en proyecciones de subespacios de imagen, como OSPRX (Orthogonal Subspace Projection RX) y PP (Projection Pursuit). Se ha desarrollado un nuevo metodo, evaluado y contrastado por los anteriores, que supone una variacion de PP y describe el fondo espectral mediante el analisis discriminante de bandas del espectro electromagnetico, separando las anomalias con el algortimo denominado Detector de Anomalias de Fondo Termico o DAFT aplicable a sensores que registran datos en el espectro emisivo. Se han evaluado los diferentes metodos de deteccion de anomalias en rangos del espectro electromagnetico del visible e infrarrojo cercano (Visible and Near Infrared-VNIR), infrarrojo de onda corta (Short Wavelenght Infrared-SWIR), infrarrojo medio (Meadle Infrared-MIR) e infrarrojo termico (Thermal Infrared-TIR). La respuesta de las superficies en las distintas longitudes de onda del espectro electromagnetico junto con su entorno, influyen en el tipo y frecuencia de las anomalias espectrales que puedan provocar. Es por ello que se han utilizado en la investigacion cubos de datos hiperepectrales procedentes de los sensores aeroportados cuya estrategia y diseno en la construccion espectrometrica de la imagen difiere. Se han evaluado conjuntos de datos de test de los sensores AHS (Airborne Hyperspectral System), HyMAP Imaging Spectrometer, CASI (Compact Airborne Spectrographic Imager), AVIRIS (Airborne Visible Infrared Imaging Spectrometer), HYDICE (Hyperspectral Digital Imagery Collection Experiment) y MASTER (MODIS/ASTER Simulator). Se han disenado experimentos sobre ambitos naturales, urbanos y semiurbanos de diferente complejidad. Se ha evaluado el comportamiento de los diferentes detectores de anomalias a traves de 23 tests correspondientes a 15 areas de estudio agrupados en 6 espacios o escenarios: Urbano - E1, Semiurbano/Industrial/Periferia Urbana - E2, Forestal - E3, Agricola - E4, Geologico/Volcanico - E5 y Otros Espacios Agua, Nubes y Sombras - E6. El tipo de sensores evaluados se caracteriza por registrar imagenes en un amplio rango de bandas, estrechas y contiguas, del espectro electromagnetico. La Tesis se ha centrado en el desarrollo de tecnicas que permiten separar y extraer automaticamente pixeles o grupos de pixeles cuya firma espectral difiere de manera discriminante de las que tiene alrededor, adoptando para ello como espacio muestral parte o el conjunto de las bandas espectrales en las que ha registrado radiancia el sensor hiperespectral. Un factor a tener en cuenta en la investigacion ha sido el propio instrumento de medida, es decir, la caracterizacion de los distintos subsistemas, sensores imagen y auxiliares, que intervienen en el proceso. Para poder emplear cuantitativamente los datos medidos ha sido necesario definir las relaciones espaciales y espectrales del sensor con la superficie observada y las potenciales anomalias y patrones objetivos de deteccion. Se ha analizado la repercusion que en la deteccion de anomalias tiene el tipo de sensor, tanto en su configuracion espectral como en las estrategias de diseno a la hora de registrar la radiacion prodecente de las superficies, siendo los dos tipos principales de sensores estudiados los barredores o escaneres de espejo giratorio (whiskbroom) y los barredores o escaneres de empuje (pushbroom). Se han definido distintos escenarios en la investigacion, lo que ha permitido abarcar una amplia variabilidad de entornos geomorfologicos y de tipos de coberturas, en ambientes mediterraneos, de latitudes medias y tropicales. En resumen, esta Tesis presenta una tecnica de deteccion de anomalias para datos hiperespectrales denominada DAFT en su variante de PP, basada en una reduccion de la dimensionalidad proyectando el fondo en un rango de longitudes de onda del espectro termico distinto de la proyeccion de las anomalias u objetivos sin firma espectral conocida. La metodologia propuesta ha sido probada con imagenes hiperespectrales reales de diferentes sensores y en diferentes escenarios o espacios, por lo tanto de diferente fondo espectral tambien, donde los resultados muestran los beneficios de la aproximacion en la deteccion de una gran variedad de objetos cuyas firmas espectrales tienen suficiente desviacion respecto del fondo. La tecnica resulta ser automatica en el sentido de que no hay necesidad de ajuste de parametros, dando resultados significativos en todos los casos. Incluso los objetos de tamano subpixel, que no pueden distinguirse a simple vista por el ojo humano en la imagen original, pueden ser detectados como anomalias. Ademas, se realiza una comparacion entre el enfoque propuesto, la popular tecnica RX y otros detectores tanto en su modalidad global como local. El metodo propuesto supera a los demas en determinados escenarios, demostrando su capacidad para reducir la proporcion de falsas alarmas. Los resultados del algoritmo automatico DAFT desarrollado, han demostrado la mejora en la definicion cualitativa de las anomalias espectrales que identifican a entidades diferentes en o bajo superficie, reemplazando para ello el modelo clasico de distribucion normal con un metodo robusto que contempla distintas alternativas desde el momento mismo de la adquisicion del dato hiperespectral. Para su consecucion ha sido necesario analizar la relacion entre parametros biofisicos, como la reflectancia y la emisividad de los materiales, y la distribucion espacial de entidades detectadas respecto de su entorno. Por ultimo, el algoritmo DAFT ha sido elegido como el mas adecuado para sensores que adquieren datos en el TIR, ya que presenta el mejor acuerdo con los datos de referencia, demostrando una gran eficacia computacional que facilita su implementacion en un sistema de cartografia que proyecte de forma automatica en un marco geografico de referencia las anomalias detectadas, lo que confirma un significativo avance hacia un sistema en lo que se denomina cartografia en tiempo real. The aim of this Thesis is to develop a specific methodology in order to be applied in automatic detection anomalies processes using hyperspectral data also called hyperspectral scenes, and to improve the classification processes. Several scenarios, areas and their relationship with surfaces and objects have been tested. The spectral characteristics of reflectance parameter and emissivity in the pattern recognition of urban materials in several hyperspectral scenes have also been tested. Spectral ranges of the visible-near infrared (VNIR), shortwave infrared (SWIR) and thermal infrared (TIR) from hyperspectral data cubes of AHS (Airborne Hyperspectral System), HyMAP Imaging Spectrometer, CASI (Compact Airborne Spectrographic Imager), AVIRIS (Airborne Visible Infrared Imaging Spectrometer), HYDICE (Hyperspectral Digital Imagery Collection Experiment) and MASTER (MODIS/ASTER Simulator) have been used in this research. It is assumed that there is not prior knowledge of the targets in anomaly detection. Thus, the pixels are automatically separated according to their spectral information, significantly differentiated with respect to a background, either globally for the full scene, or locally by the image segmentation. Several experiments on different scenarios have been designed, analyzing the behavior of the standard RX anomaly detector and different methods based on subspace, image projection and segmentation-based anomaly detection methods. Results and their consequences in unsupervised classification processes are discussed. Detection of spectral anomalies aims at extracting automatically pixels that show significant responses in relation of their surroundings. This Thesis deals with the unsupervised technique of target detection, also called anomaly detection. Since this technique assumes no prior knowledge about the target or the statistical characteristics of the data, the only available option is to look for objects that are differentiated from the background. Several methods have been developed in the last decades, allowing a better understanding of the relationships between the image dimensionality and the optimization of search procedures as well as the subpixel differentiation of the spectral mixture and its implications in anomalous responses. In other sense, image spectrometry has proven to be efficient in the characterization of materials, based on statistical methods using a specific reflection and absorption bands. Spectral configurations in the VNIR, SWIR and TIR have been successfully used for mapping materials in different urban scenarios. There has been an increasing interest in the use of high resolution data (both spatial and spectral) to detect small objects and to discriminate surfaces in areas with urban complexity. This has come to be known as target detection which can be either supervised or unsupervised. In supervised target detection, algorithms lean on prior knowledge, such as the spectral signature. The detection process for matching signatures is not straightforward due to the complications of converting data airborne sensor with material spectra in the ground. This could be further complicated by the large number of possible objects of interest, as well as uncertainty as to the reflectance or emissivity of these objects and surfaces. An important objective in this research is to establish relationships that allow linking spectral anomalies with what can be called informational anomalies and, therefore, identify information related to anomalous responses in some places rather than simply spotting differences from the background. The development in recent years of new hyperspectral sensors and techniques, widen the possibilities for applications in remote sensing of the Earth. Remote sensing systems measure and record electromagnetic disturbances that the surveyed objects induce in their surroundings, by means of different sensors mounted on airborne or space platforms. Map updating is important for management and decisions making people, because of the fast changes that usually happen in natural, urban and semi urban areas. It is necessary to optimize the methodology for obtaining the best from remote sensing techniques from hyperspectral data. The first problem with hyperspectral data is to reduce the dimensionality, keeping the maximum amount of information. Hyperspectral sensors augment considerably the amount of information, this allows us to obtain a better precision on the separation of material but at the same time it is necessary to calculate a bigger number of parameters, and the precision lowers with the increase in the number of bands. This is known as the Hughes effects (Bellman, 1957) . Hyperspectral imagery allows us to discriminate between a huge number of different materials however some land and urban covers are made up with similar material and respond similarly which produces confusion in the classification. The training and the algorithm used for mapping are also important for the final result and some properties of thermal spectrum for detecting land cover will be studied. In summary, this Thesis presents a new technique for anomaly detection in hyperspectral data called DAFT, as a PP's variant, based on dimensionality reduction by projecting anomalies or targets with unknown spectral signature to the background, in a range thermal spectrum wavelengths. The proposed methodology has been tested with hyperspectral images from different imaging spectrometers corresponding to several places or scenarios, therefore with different spectral background. The results show the benefits of the approach to the detection of a variety of targets whose spectral signatures have sufficient deviation in relation to the background. DAFT is an automated technique in the sense that there is not necessary to adjust parameters, providing significant results in all cases. Subpixel anomalies which cannot be distinguished by the human eye, on the original image, however can be detected as outliers due to the projection of the VNIR end members with a very strong thermal contrast. Furthermore, a comparison between the proposed approach and the well-known RX detector is performed at both modes, global and local. The proposed method outperforms the existents in particular scenarios, demonstrating its performance to reduce the probability of false alarms. The results of the automatic algorithm DAFT have demonstrated improvement in the qualitative definition of the spectral anomalies by replacing the classical model by the normal distribution with a robust method. For their achievement has been necessary to analyze the relationship between biophysical parameters such as reflectance and emissivity, and the spatial distribution of detected entities with respect to their environment, as for example some buried or semi-buried materials, or building covers of asbestos, cellular polycarbonate-PVC or metal composites. Finally, the DAFT method has been chosen as the most suitable for anomaly detection using imaging spectrometers that acquire them in the thermal infrared spectrum, since it presents the best results in comparison with the reference data, demonstrating great computational efficiency that facilitates its implementation in a mapping system towards, what is called, Real-Time Mapping.

Probabilistic mapping with mobile robots in structured environments

La mayor parte de los entornos diseñados por el hombre presentan características geométricas específicas. En ellos es frecuente encontrar formas poligonales, rectangulares, circulares . . . con una serie de relaciones típicas entre distintos elementos del entorno. Introducir este tipo de conocimiento en el proceso de construcción de mapas de un robot móvil puede mejorar notablemente la calidad y la precisión de los mapas resultantes. También puede hacerlos más útiles de cara a un razonamiento de más alto nivel. Cuando la construcción de mapas se formula en un marco probabilístico Bayesiano, una especificación completa del problema requiere considerar cierta información a priori sobre el tipo de entorno. El conocimiento previo puede aplicarse de varias maneras, en esta tesis se presentan dos marcos diferentes: uno basado en el uso de primitivas geométricas y otro que emplea un método de representación cercano al espacio de las medidas brutas. Un enfoque basado en características geométricas supone implícitamente imponer un cierto modelo a priori para el entorno. En este sentido, el desarrollo de una solución al problema SLAM mediante la optimización de un grafo de características geométricas constituye un primer paso hacia nuevos métodos de construcción de mapas en entornos estructurados. En el primero de los dos marcos propuestos, el sistema deduce la información a priori a aplicar en cada caso en base a una extensa colección de posibles modelos geométricos genéricos, siguiendo un método de Maximización de la Esperanza para hallar la estructura y el mapa más probables. La representación de la estructura del entorno se basa en un enfoque jerárquico, con diferentes niveles de abstracción para los distintos elementos geométricos que puedan describirlo. Se llevaron a cabo diversos experimentos para mostrar la versatilidad y el buen funcionamiento del método propuesto. En el segundo marco, el usuario puede definir diferentes modelos de estructura para el entorno mediante grupos de restricciones y energías locales entre puntos vecinos de un conjunto de datos del mismo. El grupo de restricciones que se aplica a cada grupo de puntos depende de la topología, que es inferida por el propio sistema. De este modo, se pueden incorporar nuevos modelos genéricos de estructura para el entorno con gran flexibilidad y facilidad. Se realizaron distintos experimentos para demostrar la flexibilidad y los buenos resultados del enfoque propuesto. Abstract Most human designed environments present specific geometrical characteristics. In them, it is easy to find polygonal, rectangular and circular shapes, with a series of typical relations between different elements of the environment. Introducing this kind of knowledge in the mapping process of mobile robots can notably improve the quality and accuracy of the resulting maps. It can also make them more suitable for higher level reasoning applications. When mapping is formulated in a Bayesian probabilistic framework, a complete specification of the problem requires considering a prior for the environment. The prior over the structure of the environment can be applied in several ways; this dissertation presents two different frameworks, one using a feature based approach and another one employing a dense representation close to the measurements space. A feature based approach implicitly imposes a prior for the environment. In this sense, feature based graph SLAM was a first step towards a new mapping solution for structured scenarios. In the first framework, the prior is inferred by the system from a wide collection of feature based priors, following an Expectation-Maximization approach to obtain the most probable structure and the most probable map. The representation of the structure of the environment is based on a hierarchical model with different levels of abstraction for the geometrical elements describing it. Various experiments were conducted to show the versatility and the good performance of the proposed method. In the second framework, different priors can be defined by the user as sets of local constraints and energies for consecutive points in a range scan from a given environment. The set of constraints applied to each group of points depends on the topology, which is inferred by the system. This way, flexible and generic priors can be incorporated very easily. Several tests were carried out to demonstrate the flexibility and the good results of the proposed approach.

Unsupervised system to classify SO2 pollutant concentrations in Salamanca, Mexico

Salamanca is cataloged as one of the most polluted cities in Mexico. In order to observe the behavior and clarify the influence of wind parameters on the Sulphur Dioxide (SO2) concentrations a Self-Organizing Maps (SOM) Neural Network have been implemented at three monitoring locations for the period from January 1 to December 31, 2006. The maximum and minimum daily values of SO2 concentrations measured during the year of 2006 were correlated with the wind parameters of the same period. The main advantages of the SOM Neural Network is that it allows to integrate data from different sensors and provide readily interpretation results. Especially, it is powerful mapping and classification tool, which others information in an easier way and facilitates the task of establishing an order of priority between the distinguished groups of concentrations depending on their need for further research or remediation actions in subsequent management steps. For each monitoring location, SOM classifications were evaluated with respect to pollution levels established by Health Authorities. The classification system can help to establish a better air quality monitoring methodology that is essential for assessing the effectiveness of imposed pollution controls, strategies, and facilitate the pollutants reduction.

Self-positioning and mapping of rectangular rooms with sectorized narrowband antennas

A system for simultaneous 2D estimation of rectangular room and transceiver localization is proposed. The system is based on two radio transceivers, both capable of full duplex operations (simultaneous transmission and reception). This property enables measurements of channel impulse response (CIR) at the same place the signal is transmitted (generated), commonly known as self-to-self CIR. Another novelty of the proposed system is the spatial CIR discrimination that is possible with the receiver antenna design which consists of eight sectorized antennas with 45° aperture in the horizontal plane and total coverage equal to the isotropic one. The dimensions of a rectangular room are reconstructed directly from spatial radio impulse responses by extracting the information regarding round trip time (RTT). Using radar approach estimation of walls and corners positions is derived. Tests using measured data were performed, and the simulation results confirm the feasibility of the approach.

Integrated electronic system for ultrasonic structural health monitoring

A fully integrated on-board electronic system that can perform in-situ structural health monitoring (SHM) of aircraft?s structures using specifically designed equipment for SHM based on guided wave ultrasonic method or Lamb waves? method is introduced. This equipment is called Phased Array Monitoring for Enhanced Life Assessment (PAMELA III) and is an essential part of overall PAMELA SHM? system. PAMELA III can generate any kind of excitation signals, acquire the response signals that propagate throughout the structure being tested, and perform the signal processing for damage detection directly on the structure without need to send the huge amount of raw signals but only the final SHM maps. It monitors the structure by means of an array of integrated Phased Array (PhA) transducers preferably bonded onto the host structure. The PAMELA III hardware for SHM mapping has been designed, built and subjected to laboratory tests, using aluminum and CFRP structures. The 12 channel system has been designed to be low weight (265 grams only), to have a small form factor, to be directly mounted above the integrated PhA transducers without need for cables and to be EMI protected so that the equipment can be taken on board an aircraft to perform required SHM analyses by use of embedded SHM algorithms. Moreover, the autonomous, automatic and on real-time working procedure makes it suitable for the avionic field, sending the corresponding alerts, maps and reports to external equipment.

From Analysis Model to Software Architecture: a PIM2PIM Mapping.

To our knowledge, no current software development methodology explicitly describes how to transit from the analysis model to the software architecture of the application. This paper presents a method to derive the software architecture of a system from its analysis model. To do this, we are going to use MDA. Both the analysis model and the architectural model are PIMs described with UML 2. The model type mapping designed consists of several rules (expressed using OCL and natural language) that, when applied to the analysis artifacts, generate the software architecture of the application. Specifically the rules act on elements of the UML 2 metamodel (metamodel mapping). We have developed a tool (using Smalltalk) that permits the automatic application of these rules to an analysis model defined in RoseTM to generate the application architecture expressed in the architectural style C2.