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This thesis describes the theoretical, experimental, and practical aspects of a noise-limited method for scene-change detection in images. The research is divided into three sections: noise analysis and modelling, dual illumination scene-change modelling, and integration of noise into the scene-change model. The sources of noise within commercially available digital cameras are described, with a new model for image noise derived for charge-coupled device (CCD) cameras. The model is validated experimentally through the development of techniques that allow the individual noise components to be measured from the analysis of output images alone. A generic model for complementary metal-oxide-semiconductor (CMOS) cameras is also derived. Methods for the analysis of spatial (inter-pixel) and temporal (intra-pixel) noise are developed. These are used subsequently to investigate the effects of environmental temperature on camera noise. Based on the cameras tested, the results show that the CCD camera noise response to variation in environmental temperature is complex whereas the CMOS camera response simply increases monotonically. A new concept for scene-change detection is proposed based upon a dual illumination concept where both direct and ambient illumination sources are present in an environment, such as that which occurs in natural outdoor scenes with direct sunlight and ambient skylight. The transition of pixel colour from the combined direct and ambient illuminants to the ambient illuminant only is modelled. A method for shadow-free scene-change is then developed that predicts a pixel's colour when the area in the scene is subjected to ambient illumination only, allowing pixel change to be distinguished as either being due to a cast shadow or due to a genuine change in the scene. Experiments on images captured in controlled lighting demonstrate 91% of scene-change and 83% of cast shadows are correctly determined from analysis of pixel colour change alone. A statistical method for detecting shadow-free scene-change is developed. This is achieved by bounding the dual illumination model by the confidence interval associated with the pixel's noise. Three benefits arise from the integration of noise into the scene-change detection method: - The necessity for pre-filtering images for noise is removed; - All empirical thresholds are removed; and - Performance is improved. The noise-limited scene-change detection algorithm correctly classifies 93% of scene-change and 87% of cast shadows from pixel colour change alone. When simple post-analysis size-filtering is applied both these figures increase to 95%.

The aim of this thesis was to help elucidate the mechanisms that underlie age-related decline in visual selective attention and working memory (WM). Older and younger adults completed a behavioural WM task, after which electroencephalogram (EEG) was recorded as participants perform a localized attentional interference (LAI) task – competition/attentional interference was manipulated by systematically altering the distance between targets and distractors. Older adults showed impaired accuracy and reaction time on the WM and LAI tasks. Two event-related-potentials, indexing spatial attention (N2pc) and target processing (Ptc), displayed attenuated amplitude and increased latency in older adults. Thus, spatial selection, target enhancement and processing speed deficits may contribute to age-related attentional impairments. Furthermore, an unexpected component was found between the N2pc and Ptc in the older adult waveforms. Preliminary analyses suggest this may be the PD, implicated in distractor suppression, which may be differentially contributing to older and younger adults’ electrophysiology and attentional processing.

The aim of this thesis was to help elucidate the mechanisms that underlie age-related decline in visual selective attention and working memory (WM). Older and younger adults completed a behavioural WM task, after which electroencephalogram (EEG) was recorded as participants perform a localized attentional interference (LAI) task – competition/attentional interference was manipulated by systematically altering the distance between targets and distractors. Older adults showed impaired accuracy and reaction time on the WM and LAI tasks. Two event-related-potentials, indexing spatial attention (N2pc) and target processing (Ptc), displayed attenuated amplitude and increased latency in older adults. Thus, spatial selection, target enhancement and processing speed deficits may contribute to age-related attentional impairments. Furthermore, an unexpected component was found between the N2pc and Ptc in the older adult waveforms. Preliminary analyses suggest this may be the PD, implicated in distractor suppression, which may be differentially contributing to older and younger adults’ electrophysiology and attentional processing.

Cette thèse est le fruit de l’interaction de deux disciplines qui sont la détection de changements dans des images multitemporelles et le raisonnement évidentiel à l’aide de la théorie de Dempster-Shafer (DST). Aborder le problème de détection et d’analyse de changements par la DST nécessite la détermination d’un cadre de discernement exhaustif et exclusif. Ce problème s’avère complexe en l’absence des informations a priori sur les images. Nous proposons dans ce travail de recherche un nouvel algorithme de clustering basé sur l’algorithme Fuzzy-C-Means (FCM) afin de définir les classes sémantiques existantes. L’idée de cet algorithme est la représentation de chaque classe par un nombre varié de centroïdes afin de garantir une meilleure caractérisation de classes. Afin d’assurer l’exhaustivité du cadre de discernement, un nouvel indice de validité de clustering permettant de déterminer le nombre optimal de classes sémantiques est proposé. La troisième contribution consiste à exploiter la position du pixel par rapport aux centroïdes des classes et les degrés d’appartenance afin de définir la distribution de masse qui représente les informations. La particularité de la distribution proposée est la génération d’un nombre réduit des éléments focaux et le respect des axiomes mathématiques en effectuant la transformation flou-masse. Nous avons souligné la capacité du conflit évidentiel à indiquer les transformations multi-temporelles. Nous avons porté notre raisonnement sur la décomposition du conflit global et l’estimation des conflits partiels entre les couples des éléments focaux pour mesurer le conflit causé par le changement. Cette stratégie permet d’identifier le couple de classes qui participent dans le changement. Pour quantifier ce conflit, nous avons proposé une nouvelle mesure de changement notée CM. Finalement, nous avons proposé un algorithme permettant de déduire la carte binaire de changements à partir de la carte de conflits partiels. / This thesis is the interaction result of two disciplines that are the change detection in multitemporal images and the evidential reasoning using the Dempster-Shafer theory (DST). Addressing the problem of change detection and analyzing by the DST, requires the determination of an exhaustive and exclusive frame of discernment. This issue is complex when images lake prior information. In this research work, we propose a new clustering algorithm based on the Fuzzy-C-Means (FCM) algorithm in order to define existing semantic classes. The idea of this algorithm is the representation of each class by a varied number of centroids in order to guarantee a better characterization of classes. To ensure the frame of discernment exhaustiveness, we proposed a new cluster validity index able to identify the optimal number of semantic classes. The third contribution is to exploit the position of the pixel in relation to class centroids and its membership distribution in order to define the mass distribution that represents information. The particularity of the proposed distribution, is the generation of a reduced set of focal elements and the respect of mathematical axioms when performing the fuzzy-mass transformation. We have emphasized the capacity of evidential conflict to indicate multi-temporal transformations. We reasoned on the decomposition of the global conflict and the estimation of the partial conflicts between the couples of focal elements to measure the conflict caused by the change. This strategy allows to identify the couple of classes that participate in the change. To quantify this conflict, we proposed a new measure of change noted CM. Finally, we proposed an algorithm to deduce the binary map of changes from the partial conflicts map.

This thesis deals with the possibility to evaluate cognitive and emotional traits and their relations to job positions and functions. The basis is to create a set of tests which could be used during a hiring procedure while deciding among candidates. It also suggests how to classify users based on a set of tests and thereby provide support in choosing the right job. The thesis gives a brief outlook of individually tested traits, describes a proposal of a web application and its implementation, describes implementation of neural network classifier and presents obtained results.

The purpose of this research is to study the detection of temporal changes between two (or more) multimodal images satellites, i.e., between two different imaging modalities acquired by two heterogeneous sensors, giving for the same scene two images encoded differently and depending on the nature of the sensor used for each acquisition. The two (or multiple) multimodal satellite images are acquired and coregistered at two different dates, usually before and after an event. In this study, we propose new models belonging to different categories of multimodal change detection in remote sensing imagery. As a first contribution, we present a new constraint scenario expressed on every pair of pixels existing in the before and after image change. A second contribution of our work is to propose a spatio-temporal textural gradient operator expressed with complementary norms and also a new filtering strategy of the difference map resulting from this operator. Another contribution consists in constructing an observation field from a pair of pixels and to infer a solution maximum a posteriori sense. A fourth contribution is proposed which consists to build a common feature space for the two heterogeneous images. Our fifth contribution lies in the modeling of patterns of change by anomalies and on the analysis of reconstruction errors which we propose to learn a non-supervised model from a training base consisting only of patterns of no-change in order that the built model reconstruct the normal patterns (non-changes) with a small reconstruction error. In the sixth contribution, we propose a pairwise learning architecture based on a pseudosiamese CNN network that takes as input a pair of data instead of a single data and constitutes two partly uncoupled CNN parallel network streams (descriptors) followed by a decision network that includes fusion layers and a loss layer in the sense of the entropy criterion. The proposed models are enough flexible to be used effectively in the monomodal change detection case. / Cette recherche a pour objet l’étude de la détection de changements temporels entre deux (ou plusieurs) images satellitaires multimodales, i.e., avec deux modalités d’imagerie différentes acquises par deux capteurs hétérogènes donnant pour la même scène deux images encodées différemment suivant la nature du capteur utilisé pour chacune des prises de vues. Les deux (ou multiples) images satellitaires multimodales sont prises et co-enregistrées à deux dates différentes, avant et après un événement. Dans le cadre de cette étude, nous proposons des nouveaux modèles de détection de changement en imagerie satellitaire multimodale semi ou non supervisés. Comme première contribution, nous présentons un nouveau scénario de contraintes exprimé sur chaque paire de pixels existant dans l’image avant et après changement. Une deuxième contribution de notre travail consiste à proposer un opérateur de gradient textural spatio-temporel exprimé avec des normes complémentaires ainsi qu’une nouvelle stratégie de dé-bruitage de la carte de différence issue de cet opérateur. Une autre contribution consiste à construire un champ d’observation à partir d’une modélisation par paires de pixels et proposer une solution au sens du maximum a posteriori. Une quatrième contribution est proposée et consiste à construire un espace commun de caractéristiques pour les deux images hétérogènes. Notre cinquième contribution réside dans la modélisation des zones de changement comme étant des anomalies et sur l’analyse des erreurs de reconstruction dont nous proposons d’apprendre un modèle non-supervisé à partir d’une base d’apprentissage constituée seulement de zones de non-changement afin que le modèle reconstruit les motifs de non-changement avec une faible erreur. Dans la dernière contribution, nous proposons une architecture d’apprentissage par paires de pixels basée sur un réseau CNN pseudo-siamois qui prend en entrée une paire de données au lieu d’une seule donnée et est constituée de deux flux de réseau (descripteur) CNN parallèles et partiellement non-couplés suivis d’un réseau de décision qui comprend de couche de fusion et une couche de classification au sens du critère d’entropie. Les modèles proposés s’avèrent assez flexibles pour être utilisés efficacement dans le cas des données-images mono-modales.

There has been rapid change in the landcover/landuse in the Mekong delta, Viet Nam. The landcover/landuse has changed very fast due to intense population pressure, agriculture/aquaculture farming and timber collection in the coastal areas of the delta. The changing landuse pattern in the coastal areas of the delta is threatened to be flooded by sea level rise; sea level is expected to rise 33 cm until 2050; 45 cm until 2070 and 1 m until 2100. The coastline along the eastern Mekong delta has never been static, but the loss of mangrove forests along the coast has intensified coastline change. The objective of the present study is to map the changes in landcover/landuse along the eastern coast of the Mekong delta; and to detect the changes in position of the eastern coastline over the time period from 1989 to 2002.To detect changes in landuse, two satellite images of the same season, acquired by the TM sensor of Landsat 5 and the ETM+ sensor of Landsat 7 were used. The TM image was acquired on January 16, 1989 and ETM+ image was acquired on February 13, 2002. The landcover/landuse classes selected for the study are water, forest, open vegetation, soil and shrimp farms. Image differencing and post classification comparison are used to detect the changes between two time periods. Image to image correction technique is used to align satellite images. Maximum likelihood supervised classification technique is used to classify images. The result of the classification consists of five classes for 1989 and 2002, respectively. Overall accuracies of 87.5% and 86.8%, with kappa values of 0.85 and 0.84 are obtained for landuse 1989 and landuse 2002, respectively. The overall accuracy for the change map is 82% with kappa value 0.80. Post classification comparison is carried out in this study based on the supervised classification results. According to the results obtained from the post classification comparison, a significant decrease of 48% in forest and a significant increase of 74% in open vegetation and 21% in shrimp farms area observed over the entire study area. The coastline obtained by the combination of histogram thresholding and band ratio showed an overall advancement towards the South China Sea. The results showed that new land patches emerged along the eastern coast. The amount of new land patches appeared along the coast of the Mekong delta is approximately 2% of the entire study area.

Measures of workload have been developed on the basis of the various definitions, some are designed to capture the multi-dimensional aspects of a unitary resource pool (Kahneman, 1973) while others are developed on the basis of multiple resource theory (Wickens, 2002). Although many theory based workload measures exist, others have often been constructed to serve the purpose of specific experimental tasks. As a result, it is likely that not every workload measure is reliable and valid for all tasks, much less each domain. To date, no single measure, systematically tested across experimental tasks, domains, and other measures is considered a universal measure of workload. Most researchers would argue that multiple measures from various categories should be applied to a given task to comprehensively assess workload. The goal for Study 1 to establish task load manipulations for two theoretically different tasks that induce distinct levels of workload assessed by both subjective and performance measures was successful. The results of the subjective responses support standardization and validation of the tasks and demands of that task for investigating workload. After investigating the use of subjective and objective measures of workload to identify a universal and comprehensive measure or set of measures, based on Study 2, it can only be concluded that not one or a set of measures exists. Arguably, it is not to say that one will never be conceived and developed, but at this time, one does not reside in the psychometric catalog. Instead, it appears that a more suitable approach is to customize a set of workload measures based on the task. The novel approach of assessing the sensitivity and comprehensive ability of conjointly utilizing subjective, performance, and physiological workload measures for theoretically different tasks within the same domain contributes to the theory by laying the foundation for improving methodology for researching workload. The applicable contribution of this project is a stepping-stone towards developing complex profiles of workload for use in closed-loop systems, such as human-robot team iv interaction. Identifying the best combination of workload measures enables human factors practitioners, trainers, and task designers to improve methodology and evaluation of system designs, training requirements, and personnel selection

Der Amazonas befindet sich im Wandel. Seine Regenwälder sind zunehmend durch die expandierende Landwirtschaft bedroht. Brandrodungen und die meist extensive Weidewirtschaft verantworten großflächige Ökosystemschäden und hohe Treibhausgasemissionen. Erdbeobachtungssysteme wie die Landsat und Sentinel-2 Satelliten ermöglichen eine großflächige Analyse dieser Entwicklungen und sind unerlässlich zur Evaluierung von Maßnahmen zum Schutze des Amazonas. Allerdings sind in den Kerntropen Fernerkundungsanalysen aufgrund des Bewölkungsgrades sehr herausfordernd. Diese Arbeit zielt daher auf eine verbesserte Erkennung landwirtschaftlicher Prozesse, wie sie an Entwaldungsfronten und speziell in der Region Novo Progresso, Pará, Brasilien, typisch sind. Dazu wurde zunächst der EO Time Series Explorer entwickelt, um verschiedene Dimensionen dichter Multisensorzeitserien interaktiv zur Erstellung von Referenzdaten in Wert zu setzen. Mit den Clear Observation Sequences (COS) wurde darauf basierend ein neuer Ansatz zur Erfassung hoch-dynamischer landwirtschaftlicher Prozesse entwickelt, etwa Feuer mit geringer Brandlast oder Bodenbearbeitungsmaßnahmen. Darauf aufbauend wurde schließlich der Landnutzungswandel in der Region Novo Progresso zwischen 2014 und 2020 untersucht. Die Ergebnisse zeigen einen alarmierenden Anstieg der Entwaldung und eine Zunahme landwirtschaftlicher Feuer seit der Präsidentschaft von Jair Bolsonaro. Differenziert nach Landnutzungszonen und Betriebsgrößen wird deutlich, dass Schutzgebiete weniger wirksam sind und insbesondere größere Landwirtschaftsbetriebe die Entwaldung vorantreiben. Diese Arbeit zeigt den hohen Wert einer synergetischen Nutzung unterschiedlicher Satellitenzeitserien für die fernerkundliche Analyse landwirtschaftlicher Prozesse. Eine weitere Verdichtung der Zeitserien mit räumlich und spektral höherauflösenden Sensoren bietet weiteres Verbesserungspotential bei der Beschreibung landwirtschaftlicher Dynamiken. / The Amazon is in transition, and its rainforests are increasingly threatened by agricultural expansion. A slash-and-burn agriculture and mostly extensive cattle grazing are responsible for large-scale ecosystem damage and high levels of greenhouse gas emission. Earth observation systems such as the Landsat and Sentinel-2 satellites enable large-scale analysis of these developments and are essential for evaluating measures to protect the Amazon. However, cloud cover makes remote sensing analysis challenging in the core tropics. The present work aims to improve the detection of agricultural processes typical of deforestation frontiers, focusing specifically on the Novo Progresso region, Pará, Brazil. To that end, the EO Time Series Explorer was developed to interactively visualize the different dimensions of dense multi-sensor time series and to create reference data. Based on this software tool, the Clear Observation Sequences (COS) approach was developed to capture highly dynamic agricultural processes such as low-load fires or tillage operations. Finally, the investigation of land-use changes in the Novo Progresso region between 2014 and 2020 shows an alarming increase in deforestation and agricultural fires since Jair Bolsonaro’s accession to the presidency. Analysis by land-use zone and property size shows that protected areas have become less effective and that larger properties are driving deforestation. This work demonstrates the value of synergistic use of satellite time series for remote sensing analysis of agricultural processes. Further densification of time series using higher spatial and spectral resolution sensors promises to further improve the description of agricultural dynamics.