Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

Generalising history matching for enhanced calibration of computer models

History matching using Gaussian process emulators is a well-known methodology for the calibration of computer models. It attempts to identify the parts of input parameter space that are likely to result in mismatches between simulator outputs and physical observations by using emulators. These parts are then ruled out. The remaining “Not Ruled Out Yet (NROY)” input space is then searched for good matches by repeating the history matching process. The first section of this thesis illustrates an easily neglected limitation of standard history matching: the emulator must simulate the target NROY space well, else good parameter choices can be ruled out. We show that even when an emulator passes standard diagnostic checks on the whole parameter space, good parameter choices can easily be ruled out. We present novel methods for detecting these cases and a Local Voronoi Tessellation method for a robust approach to calibration that ensures that the true NROY space is retained and parameter inference is not biased. The remainder of this thesis looks into developing a generalised history matching for calibrating computer models with high-dimensional output. We address another limitation of the standard (PCA-based) history matching, which only works well when the parameters are responsible for the strength of various patterns. We show that when the parameters control the position of patterns, e.g. shifting currents, current approaches will not generally be able to calibrate these models. To overcome this, we extend history matching to kernel feature space, where output space for moving patterns can be compared with the observations. We develop kernel-based history matching as a generalisation to history matching and examine the multiple possible interpretations of the usual implausibility measure and threshold for defining NROY. Automatic kernel selection based on expert modeller judgement is introduced to enable the experts to define important features that the model should be able to reproduce

The data concept behind the data: From metadata models and labelling schemes towards a generic spectral library

Spectral libraries play a major role in imaging spectroscopy. They are commonly used to store end-member and spectrally pure material spectra, which are primarily used for mapping or unmixing purposes. However, the development of spectral libraries is time consuming and usually sensor and site dependent. Spectral libraries are therefore often developed, used and tailored only for a specific case study and only for one sensor. Multi-sensor and multi-site use of spectral libraries is difficult and requires technical effort for adaptation, transformation, and data harmonization steps. Especially the huge amount of urban material specifications and its spectral variations hamper the setup of a complete spectral library consisting of all available urban material spectra. By a combined use of different urban spectral libraries, besides the improvement of spectral inter- and intra-class variability, missing material spectra could be considered with respect to a multi-sensor/ -site use. Publicly available spectral libraries mostly lack the metadata information that is essential for describing spectra acquisition and sampling background, and can serve to some extent as a measure of quality and reliability of the spectra and the entire library itself. In the GenLib project, a concept for a generic, multi-site and multi-sensor usable spectral library for image spectra on the urban focus was developed. This presentation will introduce a 1) unified, easy-to-understand hierarchical labeling scheme combined with 2) a comprehensive metadata concept that is 3) implemented in the SPECCHIO spectral information system to promote the setup and usability of a generic urban spectral library (GUSL). The labelling scheme was developed to ensure the translation of individual spectral libraries with their own labelling schemes and their usually varying level of details into the GUSL framework. It is based on a modified version of the EAGLE classification concept by combining land use, land cover, land characteristics and spectral characteristics. The metadata concept consists of 59 mandatory and optional attributes that are intended to specify the spatial context, spectral library information, references, accessibility, calibration, preprocessing steps, and spectra specific information describing library spectra implemented in the GUSL. It was developed on the basis of existing metadata concepts and was subject of an expert survey. The metadata concept and the labelling scheme are implemented in the spectral information system SPECCHIO, which is used for sharing and holding GUSL spectra. It allows easy implementation of spectra as well as their specification with the proposed metadata information to extend the GUSL. Therefore, the proposed data model represents a first fundamental step towards a generic usable and continuously expandable spectral library for urban areas. The metadata concept and the labelling scheme also build the basis for the necessary adaptation and transformation steps of the GUSL in order to use it entirely or in excerpts for further multi-site and multi-sensor applications

Analyse hiérarchique d'images multimodales

There is a growing interest in the development of adapted processing tools for multimodal images (several images acquired over the same scene with different characteristics). Allowing a more complete description of the scene, multimodal images are of interest in various image processing fields, but their optimal handling and exploitation raise several issues. This thesis extends hierarchical representations, a powerful tool for classical image analysis and processing, to multimodal images in order to better exploit the additional information brought by the multimodality and improve classical image processing techniques. %when applied to real applications. This thesis focuses on three different multimodalities frequently encountered in the remote sensing field. We first investigate the spectral-spatial information of hyperspectral images. Based on an adapted construction and processing of the hierarchical representation, we derive a segmentation which is optimal with respect to the spectral unmixing operation. We then focus on the temporal multimodality and sequences of hyperspectral images. Using the hierarchical representation of the frames in the sequence, we propose a new method to achieve object tracking and apply it to chemical gas plume tracking in thermal infrared hyperspectral video sequences. Finally, we study the sensorial multimodality, being images acquired with different sensors. Relying on the concept of braids of partitions, we propose a novel methodology of image segmentation, based on an energetic minimization framework.Il y a un intérêt grandissant pour le développement d’outils de traitements adaptés aux images multimodales (plusieurs images de la même scène acquises avec différentes caractéristiques). Permettant une représentation plus complète de la scène, ces images multimodales ont de l'intérêt dans plusieurs domaines du traitement d'images, mais les exploiter et les manipuler de manière optimale soulève plusieurs questions. Cette thèse étend les représentations hiérarchiques, outil puissant pour le traitement et l’analyse d’images classiques, aux images multimodales afin de mieux exploiter l’information additionnelle apportée par la multimodalité et améliorer les techniques classiques de traitement d’images. Cette thèse se concentre sur trois différentes multimodalités fréquemment rencontrées dans le domaine de la télédétection. Nous examinons premièrement l’information spectrale-spatiale des images hyperspectrales. Une construction et un traitement adaptés de la représentation hiérarchique nous permettent de produire une carte de segmentation de l'image optimale vis-à-vis de l'opération de démélange spectrale. Nous nous concentrons ensuite sur la multimodalité temporelle, traitant des séquences d’images hyperspectrales. En utilisant les représentations hiérarchiques des différentes images de la séquence, nous proposons une nouvelle méthode pour effectuer du suivi d’objet et l’appliquons au suivi de nuages de gaz chimique dans des séquences d’images hyperspectrales dans le domaine thermique infrarouge. Finalement, nous étudions la multimodalité sensorielle, c’est-à-dire les images acquises par différents capteurs. Nous appuyant sur le concept des tresses de partitions, nous proposons une nouvelle méthodologie de segmentation se basant sur un cadre de minimisation d’énergie

ADAPTIVE PROCESSING ARCHITECTURE OF MULTISENSOR SIGNALS FOR LOW-IMPACT TREATMENTS OF PLANT DISEASES.

Intelligent sensing for production of high-value crops Scientific and technical quality This thesis has been realized within the CROPS project. CROPS will develop scientific know-how for a highly configurable, modular and clever carrier platform that includes modular parallel manipulators and intelligent tools (sensors, algorithms, sprayers, grippers) that can be easily installed onto the carrier and are capable of adapting to new tasks and conditions. Several technological demonstrators will be developed for high value crops like greenhouse vegetables, fruits in orchards, and grapes for premium wines. The CROPS robotic platform will be capable of site-specific spraying (targets spray only towards foliage and selective targets) and selective harvesting of fruit (detects the fruit, determines its ripeness, moves towards the fruit, grasps it and softly detaches it). Another objective of CROPS is to develop techniques for reliable detection and classification of obstacles and other objects to enable successful autonomous navigation and operation in plantations and forests. The agricultural and forestry applications share many research areas, primarily regarding sensing and learning capabilities. The project started in October 2010 and will run for 48 month. The aim of this thesis is to lay the foundations, suggesting the guidelines, of one task addressed by the CROPS project, in particular, the aim of this work is to study the application of a VIS-NIR imaging approach (intelligent sensing), based on a relatively simple algorithm, to detect symptoms of powdery mildew and downy mildew disease at early stages of infection (sustainable production of high-value crops). Also a preliminary work for botrytis detection will be shown. Concept and objectives Many site-specific agricultural and forestry tasks, such as cultivating, transplanting, spraying, trimming, selective harvesting, and transportation, could be performed more efficiently if carried out by robotic systems. However, to date, agriculture and forestry robots are still not available, partly due to the complex, and often contradictory, demands for developing such systems. On the one hand, agro-forestry robots must be of reasonable cost, but on the other, they must be able to deal with complex, dynamic, and partly changing tasks. Addressing problems such as continuously changing conditions (e.g., rain and illumination), high variability in both the products (size, and shape) and the environment (location and soil properties), the delicate nature of the products, and hostile environmental conditions (e.g. dust, dirt, extreme temperature and humidity) requires advanced sensing, manipulation, and control. Since it is impossible to model a-priori all environments and task conditions, the robot must be able to learn new tasks and new working conditions. The solution to these demands lies in a modular and configurable design that will keep costs to a minimum by applying a basic configuration to a range of agricultural applications. At least a 95% yield rate is necessary for economical feasibility of an agro-forestry robotic system. Objectives An objective of CROPS project is to develop an \u201cintelligent tools\u201d (sensors, algorithms, sprayers) that can easily be installed onto a modular and clever carrier platform. The CROPS robotic platform will be capable of site-specific spraying (targeted spraying only on foliage and selected targets). Research efforts To achieve the novel systems described above, we will focus on intelligent sensing of disease detection on crop canopy (investigating different types and/or multiple sensors with decision making models). Technology evaluation Technology evaluation of the developed systems will include the performance evaluation of the different components (e.g., capacities, success rates/misses). Progress beyond the state-of-the-art Despite the extensive research conducted to date in applying robots to a variety of agriculture and forestry tasks (e.g., transplanting, spraying, trimming, selective harvesting), limited operating efficiencies (speeds, success rates) and lack of economic justification have severely limited commercialization. The few commercial autonomous agriculture and forestry robots that are available on the market include a cow milking robot, a robot for cutting roses (RomboMatic), and various remote-controlled forest harvesters. These robots either have a low level of autonomy or are able to perform only simple operations in structured and static environments (e.g. dairy farms and plant breeding facilities). Developing capabilities for robots operating in unstructured outdoor environments or dealing with the highly variable objects that exist in agriculture and forestry is still open-ended, and one of CROPS aims is to address this problem. Current state-of-the-art Field trials have routinely shown that most crop damage due to diseases and pests can be efficiently controlled when treatments are applied timely and accurately by hand to susceptible targets (i.e., by intelligent spraying). Site-specific spraying targeted solely to trees and/or to infected areas can reduce pesticide use by 20\u201340%. An issue of relevance to targeted agriculture is the detection of diseases in field crops. Since such events often have a visual manifestation, state-of-the-art methods for achieving this goal include fluorescence imaging or the analysis of spectral reflectance in carefully selected spectral bands. While reports of these methods used separately achieved performance at 75\u201390% accuracy, attempts to combine them have boosted disease discrimination accuracy to 95%. We must note here, however, that despite these promising results, very little research has been conducted on in-field disease detection. Expected progress The diseased detection approach for precision pesticide spraying will be developed investigating image processing techniques (after a laboratory spectral evaluation and greenhouse testing) for high-precision close-range targeted spraying to selectively and precisely apply chemicals solely to targets susceptible to specific diseases/pests, with a mean 90% success rate. Local changes in spectral reflection of parts of the canopy will be used as an indication of disease. \u201cSoft-sensor\u201d for detection of ripeness and diseases (noncontact rapid sensing system) will be developed by multispectral sensor (multispectral spectral camera). These \u201csoft sensor\u201d can be used as a decision model for targeted spraying

UAVs for the Environmental Sciences

This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application

Interactive models for latent information discovery in satellite images

The recent increase in Earth Observation (EO) missions has resulted in unprecedented volumes of multi-modal data to be processed, understood, used and stored in archives. The advanced capabilities of satellite sensors become useful only when translated into accurate, focused information, ready to be used by decision makers from various fields. Two key problems emerge when trying to bridge the gap between research, science and multi-user platforms: (1) The current systems for data access permit only queries by geographic location, time of acquisition, type of sensor, but this information is often less important than the latent, conceptual content of the scenes; (2) simultaneously, many new applications relying on EO data require the knowledge of complex image processing and computer vision methods for understanding and extracting information from the data. This dissertation designs two important concept modules of a theoretical image information mining (IIM) system for EO: semantic knowledge discovery in large databases and data visualization techniques. These modules allow users to discover and extract relevant conceptual information directly from satellite images and generate an optimum visualization for this information. The first contribution of this dissertation brings a theoretical solution that bridges the gap and discovers the semantic rules between the output of state-of-the-art classification algorithms and the semantic, human-defined, manually-applied terminology of cartographic data. The set of rules explain in latent, linguistic concepts the contents of satellite images and link the low-level machine language to the high-level human understanding. The second contribution of this dissertation is an adaptive visualization methodology used to assist the image analyst in understanding the satellite image through optimum representations and to offer cognitive support in discovering relevant information in the scenes. It is an interactive technique applied to discover the optimum combination of three spectral features of a multi-band satellite image that enhance visualization of learned targets and phenomena of interest. The visual mining module is essential for an IIM system because all EO-based applications involve several steps of visual inspection and the final decision about the information derived from satellite data is always made by a human operator. To ensure maximum correlation between the requirements of the analyst and the possibilities of the computer, the visualization tool models the human visual system and secures that a change in the image space is equivalent to a change in the perception space of the operator. This thesis presents novel concepts and methods that help users access and discover latent information in archives and visualize satellite scenes in an interactive, human-centered and information-driven workflow.Der aktuelle Anstieg an Erdbeobachtungsmissionen hat zu einem Anstieg von multi-modalen Daten geführt die verarbeitet, verstanden, benutzt und in Archiven gespeichert werden müssen. Die erweiterten Fähigkeiten von Satellitensensoren sind nur dann von Entscheidungstraegern nutzbar, wenn sie in genaue, fokussierte Information liefern. Es bestehen zwei Schlüsselprobleme beim Versuch die Lücke zwischen Forschung, Wissenschaft und Multi-User-Systeme zu füllen: (1) Die aktuellen Systeme für Datenzugriffe erlauben nur Anfragen basierend auf geografischer Position, Aufzeichnungszeit, Sensortyp. Aber diese Informationen sind oft weniger wichtig als der latente, konzeptuelle Inhalt der Szenerien. (2) Viele neue Anwendungen von Erdbeobachtungsdaten benötigen Wissen über komplexe Bildverarbeitung und Computer Vision Methoden um Information verstehen und extrahieren zu können. Diese Dissertation zeigt zwei wichtige Konzeptmodule eines theoretischen Image Information Mining (IIM) Systems für Erdbeobachtung auf: Semantische Informationsentdeckung in grossen Datenbanken und Datenvisualisierungstechniken. Diese Module erlauben Benutzern das Entdecken und Extrahieren relevanter konzeptioneller Informationen direkt aus Satellitendaten und die Erzeugung von optimalen Visualisierungen dieser Informationen. Der erste Beitrag dieser Dissertation bringt eine theretische Lösung welche diese Lücke überbrückt und entdeckt semantische Regeln zwischen dem Output von state-of-the-art Klassifikationsalgorithmen und semantischer, menschlich definierter, manuell angewendete Terminologie von kartographischen Daten. Ein Satz von Regeln erkläret in latenten, linguistischen Konzepten den Inhalte von Satellitenbildern und verbinden die low-level Maschinensprache mit high-level menschlichen Verstehen. Der zweite Beitrag dieser Dissertation ist eine adaptive Visualisierungsmethode die einem Bildanalysten im Verstehen der Satellitenbilder durch optimale Repräsentation hilft und die kognitive Unterstützung beim Entdecken von relevenanter Informationen in Szenerien bietet. Die Methode ist ein interaktive Technik die angewendet wird um eine optimale Kombination von von drei Spektralfeatures eines Multiband-Satellitenbildes welche die Visualisierung von gelernten Zielen and Phänomenen ermöglichen. Das visuelle Mining-Modul ist essentiell für IIM Systeme da alle erdbeobachtungsbasierte Anwendungen mehrere Schritte von visueller Inspektion benötigen und davon abgeleitete Informationen immer vom Operator selbst gemacht werden müssen. Um eine maximale Korrelation von Anforderungen des Analysten und den Möglichkeiten von Computern sicher zu stellen, modelliert das Visualisierungsmodul das menschliche Wahrnehmungssystem und stellt weiters sicher, dass eine Änderung im Bildraum äquivalent zu einer Änderung der Wahrnehmung durch den Operator ist. Diese These präsentieret neuartige Konzepte und Methoden, die Anwendern helfen latente Informationen in Archiven zu finden und visualisiert Satellitenszenen in einem interaktiven, menschlich zentrierten und informationsgetriebenen Arbeitsprozess

Multispectral and Hyperspectral Remote Sensing Data for Mineral Exploration and Environmental Monitoring of Mined Areas

In recent decades, remote sensing technology has been incorporated in numerous mineral exploration projects in metallogenic provinces around the world. Multispectral and hyperspectral sensors play a significant role in affording unique data for mineral exploration and environmental hazard monitoring. This book covers the advances of remote sensing data processing algorithms in mineral exploration, and the technology can be used in monitoring and decision-making in relation to environmental mining hazard. This book presents state-of-the-art approaches on recent remote sensing and GIS-based mineral prospectivity modeling, offering excellent information to professional earth scientists, researchers, mineral exploration communities and mining companies

Handbook of Mathematical Geosciences

This Open Access handbook published at the IAMG's 50th anniversary, presents a compilation of invited path-breaking research contributions by award-winning geoscientists who have been instrumental in shaping the IAMG. It contains 45 chapters that are categorized broadly into five parts (i) theory, (ii) general applications, (iii) exploration and resource estimation, (iv) reviews, and (v) reminiscences covering related topics like mathematical geosciences, mathematical morphology, geostatistics, fractals and multifractals, spatial statistics, multipoint geostatistics, compositional data analysis, informatics, geocomputation, numerical methods, and chaos theory in the geosciences