The advancement of foundation models have opened up new possibilities in deep learning. These models can be adapted to new tasks and unseen domains from minimal or even no training data, making them well-suited for applications where labelled data is scarce or costly to collect. Lack of data has meant that deep learning for change detection in sonar imagery has not been used. Reliable methods for change detection of underwater environments is critical for a range of fields such as marine research and object search. Previous work in change detection for sonar imagery focus on non-deep learning methods. In this paper, we explore the use of a foundation model (Segment Anything Model) for performing change detection in imagery collected with synthetic aperture sonar (SAS). This thesis is the first case of applying Segment Anything Model to change detection in SAS imagery. The proposed method segments bi-temporal images, and change detection is then performed on the segments. In addition to a set of bi-temporal images containing real change, the model is also tested on a set of synthetic images. The proposed method shows promising results on both a real and synthetic data set.

This study used a comparison of one hundred and forty-eight historical (1888-1913) and current (2008-2014) oblique photographs from thirty-two stations to identify land cover changes that have occurred in portions of the Bow and Elbow valleys as well as surrounding Kananaskis Country region. Implications of these changes for flooding and flood management were explored. Forest cover was found to have drastically increased over the past century, particularly in the Bow valley, as did areas of direct human development. In the same time period, grasslands increased in the Elbow valley but decreased in the Bow, while regenerating areas decreased uniformly throughout both valleys. An analysis of pre (2008)-and-post (2014) flood conditions demonstrated no change in coniferous forest cover in both valleys over the 6-year period, but uncovered a decline of 20% in the Elbow and 3% in the Bow in the broadleaf/mixedwood category. The Elbow’s channel zone was larger in 2014 compared to 2008, whereas the extent of the Bow’s channel zone remained constant. However, both the Bow and Elbow’s bare exposed bars increased substantially, most likely as a result of the 2013 flood. The major source of water flows that contributed to the 2013 flood event originated in high elevation rock and scree areas, which, unlike floodplains, are elements of the watershed that cannot be manipulated over time. It is now recognized that forest cover should act as a buffer to floods. Nevertheless, the 2013 flood event occurred despite the massive buffering effect of a huge increase in older forest stands across the study area. The final discussion includes recommendations for improving flood management in the area. / Graduate / 0329, 0768, 0478 / tanya.taggarthodge@gmail.com

Ce travail s’inscrit dans le cadre d’un programme de recherches appuyé par le Conseil de recherches en sciences humaines du Canada. / Les forêts de Bornéo sont inestimables. En plus d’une faune et d’une flore riche et diversifiée, ses milieux naturels constituent d’efficaces réservoirs de carbone. En outre, la matière ligneuse qui y est abondante fait l’objet d’une exploitation intensive. Par contre, c’est le potentiel agricole de l’île qui crée le plus d’enthousiasme, principalement en ce qui concerne la culture du palmier à huile. Pour tenter de mieux comprendre et surveiller le phénomène, nous avons développé des méthodes de détection de la déforestation et de la dégradation des forêts. Ces méthodes doivent tenir compte des caractéristiques propres à l’île. C’est que Bornéo est abondamment affectée par une nébulosité constante qui complexifie considérablement son observation à partir des satellites. Malgré ces contraintes, nous avons produit une série chronologique annuelle des points chauds de déforestation et de dégradation des forêts pour les années 2000 à 2009. / Borneo’s forests are priceless. Beyond the richness and diversity of its fauna and flora, its natural habitats constitute efficient carbon reservoirs. Unfortunately, the vast forests of the island are rapidly being cut down, both by the forestry industry and the rapidly expanding oil palm industry. In this context, we’ve developed methods to detect deforestation and forest degradation in order to better understand and monitor the phenomena. In doing so, the peculiarities of Borneo, such as the persistent cloud cover, had to be accounted for. Nevertheless, we succeeded in producing a time series of the yearly forest degradation and deforestations hotspots for the year 2000 through the year 2009.

The (Plethodon nettingi) Cheat Mountain Salamander is a rare and endangered species that relies heavily on (Picea rubens) Red Spruce for habitat. P. rubens communities on the Cheat Mountain range in West Virginia have been disturbed by fires and logging, and regeneration of P. rubens stands are central to the survival of the P. netting. A supervised and unsupervised landscape classification of three Landsat images over the past 26 years was conducted to analyze change in P. rubens communities on Cheat Mountain Range. Change detection results revealed that from 1986-2012 a 52% growth increase of P. rubens stands, 18% loss, and 29% stayed the same over the last 26 years. P. rubens stands are vital habitat to the rare and endangered P. netting and regrowth of P. rubens is vital in restoring the habitat of the salamander on the Cheat Mountain. The regrowth of P. rubens on the Cheat Mountain range is critical to the survival of the P. nettingi. Identifying critical forest as it relates to salamander habitat is essential for conservation efforts. Since not all P. rubens stands are of equal significance to the P. nettingi, it is important to identify and map those that adhere to their stringent habitat needs as defined by forest fragmentation, aspect, slope, and lithology. I used spatial analysis and remote sensing techniques to define critical forest characteristics by applying a forest fragmentation model utilizing morphological image analysis, northeast and southwest aspects, moderate slopes, and limestone lithology. Patches were ranked based on this quantitative model and key P. rubens stands identified using spatial statistics. The results could aid in prioritizing research areas as well as conservation planning in regards to P. rubens and the P. nettingi. In this study, the MaxEnt modeling framework was used to predict habitat suitability for P. rubens under current conditions and under two future climate change scenarios. P. rubens distribution data was acquired from the U.S Geological Survey. Both the IPCC A1B and A2 emission scenarios of the HadCM3 global circulation model were projected to years 2040-2069 and 2070-2099. Results showed that a substantial decline in the suitability of future P. rubens habitat on the Cheat Mountain is likely under both climate change scenarios, particularly at lower elevations. By the end of the century, P. rubens is likely to be extirpated from the Cheat Mountain Range. By the end of century, the A1B and A2 scenarios predict the average habitat suitability for P. rubens on Cheat Mountain will be 0.0002 and 0.00004 respectively. Conservation as well as species migration efforts for P. rubens should be focused on areas such as Cheat Mountain to preserve this vital habitat.

Ce mémoire s'intéresse à la détection de mouvement dans une séquence d'images acquises à l'aide d'une caméra fixe. Dans ce problème, la difficulté vient du fait que les mouvements récurrents ou non significatifs de la scène tels que les oscillations d'une branche, l'ombre d'un objet ou les remous d'une surface d'eau doivent être ignorés et classés comme appartenant aux régions statiques de la scène. La plupart des méthodes de détection de mouvement utilisées à ce jour reposent en fait sur le principe bas-niveau de la modélisation puis la soustraction de l'arrière-plan. Ces méthodes sont simples et rapides mais aussi limitées dans les cas où l'arrière-plan est complexe ou bruité (neige, pluie, ombres, etc.). Cette recherche consiste à proposer une technique d'amélioration de ces algorithmes dont l'idée principale est d'exploiter et mimer deux caractéristiques essentielles du système de vision humain. Pour assurer une vision nette de l’objet (qu’il soit fixe ou mobile) puis l'analyser et l'identifier, l'œil ne parcourt pas la scène de façon continue, mais opère par une série de ``balayages'' ou de saccades autour (des points caractéristiques) de l'objet en question. Pour chaque fixation pendant laquelle l'œil reste relativement immobile, l'image est projetée au niveau de la rétine puis interprétée en coordonnées log polaires dont le centre est l'endroit fixé par l'oeil. Les traitements bas-niveau de détection de mouvement doivent donc s'opérer sur cette image transformée qui est centrée pour un point (de vue) particulier de la scène. L'étape suivante (intégration trans-saccadique du Système Visuel Humain (SVH)) consiste ensuite à combiner ces détections de mouvement obtenues pour les différents centres de cette transformée pour fusionner les différentes interprétations visuelles obtenues selon ses différents points de vue. / This master thesis revolves around motion detection in sequences recorded from a fixed camera. This situation is challenging since we must ignore insignificant recurring motions such as oscillating branches, shadows, or waves on the surface of the water. Those must be classified as belonging to the background and static. Most motion detection techniques used nowadays are based on the simple and low level principle of background modeling and subtraction. These techniques are simple and fast but they reach their limit when they have to deal with complex or noisy images (from snowy, rainy, sunny weather, etc.). This research consist of proposing a technique aiming to improve those algorithms by mimicking two essential characteristics of the Human Visual System (HVS). To obtain a clear vision of an object (static or mobile) and then to analyse and identify it, our eye doesn't analyse the scene continuously but operates through several sweeping motions, or saccades, across the object. During each moment when the eye stays fixed, the image is projected on the retina and then interpreted as described by log-polar coordinates, where the center is the point fixed by the eye. Low level detection treatment should then operate on this transformed image which is centered on a particular point of view of the scene. The second step (the trans-saccadic integration of the HVS) is to combine all those data gathered from different points of view.

Anthropogenic changes to the landscape, storm events and sea level rise are contributing to the erosion of beaches leading to an increase of the sediment load in near shore marine environments. Palm Beach, Florida is host to unique near shore hardbottom habitats. These areas are distinct from the vast expanses of surrounding sediments and play and important role of habitat and shelter for many different species. In this study, remotely sensed images from 2000-2015 were used to look at the movement of sediment and how it contributes to exposure rates of near shore hardbottom habitats in Palm Beach, Florida and how these factors affect the benthic community. GIS was used to determine areas of hardbottom with high exposure (exposed in >60% of aerial images), medium exposure (40-60%), and low exposure ( I strived to determine if one can detect a successional relationship of benthic communities in a dynamic environment with annual mapping. I also examined if areas with higher exposure rates have more complex successive communities than those with lower exposure rates, and what implications this has on near shore benthic communities. In situ surveys conducted at 117 sites determined the community structure (corals, octocorals, macroalgae, and hydroids). This study confirmed that periodic mapping was successful in identifying hardbottom burial and exposure, which fluctuate both spatially and temporally. This periodic mapping along with manual delineation did identify hardbottom burials and exposures that fluctuate between years and relate to benthic community differences. The near shore hardbottom coral reef communities aligned with the observed exposure categories with the greater coral species richness and octocoral morphologies found at sites classified as highly exposed. Statistical analyses showed differences in communities shallower and deeper than three meters’ depth. Increasing the frequency of imagery captures and in situ observation would further increase our comprehension of the metrics of hardbottom exposures in reference to community structure.

Les images de télédétection sont des images de la surface de la Terre acquises par des satellites ou des avions. Ces images sont de plus en plus disponibles et leur technologies évoluent rapidement. On peut observer une amélioration des capteurs existants, mais de nouveaux types de capteurs ont également vu le jour et ont montré des propriétés intéressantes pour le traitement d'images. Ainsi, les images multispectrales et radar sont devenues très classiques.La disponibilité de différents capteurs est très intéressante car elle permet de capturer une grande variété de propriétés des objets. Ces propriétés peuvent être exploitées pour extraire des informations plus riches sur les objets. Une des applications majeures de la télédétection est la détection de changements entre des images multi-temporelles (images de la même scène acquise à des instants différents). Détecter des changements entre des images acquises par des capteurs homogènes est un problème classique. Mais le problème de la détection de changements entre images acquises par des capteurs hétérogènes est un problème beaucoup plus difficile.Avoir des méthodes de détection de changements adaptées aux images issues de capteurs hétérogènes est nécessaire pour le traitement de catastrophes naturelles. Des bases de données constituées d'images optiques sont disponible, mais il est nécessaire d'avoir de bonnes conditions climatiques pour les acquérir. En revanche, les images radar sont accessibles rapidement quelles que soient les conditions climatiques et peuvent même être acquises de nuit. Ainsi, détecter des changements entre des images optiques et radar est un problème d'un grand intérêt en télédétection.L'intérêt de cette thèse est d'étudier des méthodes statistiques de détention de changements adaptés aux images issues de capteurs hétérogènes.Chapitre 1 rappelle ce qu'on entend par une image de télédétection et résume rapidement quelques méthodes de détection de changements disponibles dans la littérature. Les motivations à développer des méthodes de détection de changements adaptées aux images hétérogènes et les difficultés associiées sont présentés.Chapitre 2 étudie les propriétés statistiques des images en l'absence de changements. Un modèle de mélange de lois adapté aux ces images est introduit. La performance des méthodes classiques de détection de changements est également étudiée. Dans plusieurs cas, ce modèle permet d'expliquer certains défauts de certaines méthodes de la literature.Chapitre 3 étudie les propriétés des paramètres du modèle introduit au chapitre 2 en faisant l'hypothèse qu'ils appartiennent à une variété en l'absence de changements. Cette hypothèse est utilisée pour définir une mesure de similarité qui permet d'éviter les défauts des approches statistiques classiques. Une méthode permettant d'estimer cette mesure de similarité est présentée. Enfin, la stratégie de détection de changements basée sur cette mesure est validée à l'aide d'images synthétiques.Chapitre 4 étudie un algorithme Bayésien non-paramétrique (BNP) qui permet d'améliorer l'estimation de la variété introduite au chapitre 3, qui est basé sur un processus de restaurant Chinois (CRP) et un champs de Markov qui exploite la corrélation spatiale entre des pixels voisins de l'image. Une nouvelle loi a priori de Jeffrey pour le paramètre de concentration de ce CRP est définit. L'estimation des paramètres de ce nouveau modèle est effectuée à l'aide d'un échantillonneur de Gibbs de type "collapsed Gibbs sampler". La stratégie de détection de changement issue de ce modèle non-paramétrique est validée à l'aide d'images synthétiques.Le dernier chapitre est destiné à la validation des algorithmes de détection de changements développés sur des jeux d'images réelles montrant des résultats encourageant pour tous les cas d'étude. Le modèle BNP permet d'obtenir de meilleurs performances que le modèle paramétrique, mais ceci se fait au prix d'une complexité calculatoire plus importante. / Remote sensing images are images of the Earth surface acquired from satellites or air-borne equipment. These images are becoming widely available nowadays and its sensor technology is evolving fast. Classical sensors are improving in terms of resolution and noise level, while new kinds of sensors are proving to be useful. Multispectral image sensors are standard nowadays and synthetic aperture radar (SAR) images are very popular.The availability of different kind of sensors is very advantageous since it allows us to capture a wide variety of properties of the objects contained in a scene. These properties can be exploited to extract richer information about these objects. One of the main applications of remote sensing images is the detection of changes in multitemporal datasets (images of the same area acquired at different times). Change detection for images acquired by homogeneous sensors has been of interest for a long time. However the wide range of different sensors found in remote sensing makes the detection of changes in images acquired by heterogeneous sensors an interesting challenge.Accurate change detectors adapted to heterogeneous sensors are needed for the management of natural disasters. Databases of optical images are readily available for an extensive catalog of locations, but, good climate conditions and daylight are required to capture them. On the other hand, SAR images can be quickly captured, regardless of the weather conditions or the daytime. For these reasons, optical and SAR images are of specific interest for tracking natural disasters, by detecting the changes before and after the event.The main interest of this thesis is to study statistical approaches to detect changes in images acquired by heterogeneous sensors. Chapter 1 presents an introduction to remote sensing images. It also briefly reviews the different change detection methods proposed in the literature. Additionally, this chapter presents the motivation to detect changes between heterogeneous sensors and its difficulties.Chapter 2 studies the statistical properties of co-registered images in the absence of change, in particular for optical and SAR images. In this chapter a finite mixture model is proposed to describe the statistics of these images. The performance of classical statistical change detection methods is also studied by taking into account the proposed statistical model. In several situations it is found that these classical methods fail for change detection.Chapter 3 studies the properties of the parameters associated with the proposed statistical mixture model. We assume that the model parameters belong to a manifold in the absence of change, which is then used to construct a new similarity measure overcoming the limitations of classic statistical approaches. Furthermore, an approach to estimate the proposed similarity measure is described. Finally, the proposed change detection strategy is validated on synthetic images and compared with previous strategies.Chapter 4 studies Bayesian non parametric algorithm to improve the estimation of the proposed similarity measure. This algorithm is based on a Chinese restaurant process and a Markov random field taking advantage of the spatial correlations between adjacent pixels of the image. This chapter also defines a new Jeffreys prior for the concentration parameter of this Chinese restaurant process. The estimation of the different model parameters is conducted using a collapsed Gibbs sampler. The proposed strategy is validated on synthetic images and compared with the previously proposed strategy. Finally, Chapter 5 is dedicated to the validation of the proposed change detection framework on real datasets, where encouraging results are obtained in all cases. Including the Bayesian non parametric model into the change detection strategy improves change detection performance at the expenses of an increased computational cost.

Le projet INDESO (Développement de l'océanographie spatiale en Indonésie) en collaboration entre le gouvernement indonésien (Ministère des affaires maritimes et des pêches - MMAF) et la société française CLS (Collecte Localisation Satellite) promeut l'utilisation des technologies spatiales pour la surveillance des côtes et des mers indonésiennes. Cette thèse fait partie du volet sur la surveillance des récifs coralliens, mené par l'IRD (Institut de Recherche pour le Développement). L'objectif principal était de déterminer si les habitats des récifs coralliens dans l'île de Bunaken dans le nord de Sulawesi sont résilients, en utilisant i) des cartes d'habitat nouvellement conçues, ii) des données in situ et une série chronologique de 15 ans unique d'images satellites de différents capteurs très haute résolution (THR), iii) des données auxiliaires qui pourraient expliquer les changements détectés. Les résultats comprennent des cartes très détaillées de l'habitat des récifs de Bunaken (194 polygones cartographiés et un recensement de 175 habitats). L'influence de la chute du niveau de la mer sur la mortalité des coraux pendant l'événement El Niño de 2015-2016 est présentée en détail, et l'importance de ce processus est également discutée à partir de l'interprétation d'une série chronologique unique de 15 ans d'images THR. La série temporelle met en évidence des trajectoires très différentes des habitats coralliens. Nous avons conclu que le récif de Bunaken démontre une capacité de résilience et sans déphasage, mais qu'un diagnostic définitif de sa résilience reste difficile à déterminer par imagerie. Des trajectoires de l'habitat ne peuvent pas être totalement interprétées sans changer certains paradigmes de surveillance, et sans utiliser une combinaison d'observations de télédétection et de données in situ. / The INDESO (Infrastructure Development of Space Oceanography) project, in collaboration with the Indonesian Government (Ministry of Marine Affairs and Fisheries - MMAF) and the French company CLS (Collecte Localisation Satellites), promotes the use of space technologies for monitoring coastlines and Indonesian seas. This thesis is part of coral reef monitoring component, led by the IRD (Institute de Recherche pour le Développement). The main objective was to determine wether coral reef habitats on Bunaken Island in Northern Sulawesi are resilient, using (i) newly desgined habitat maps, (ii) in situ data, and a unique 15-year time series of satellite images of different very high resolution (VHR) sensors, and (iii) ancillary data that could explain the changes detected. The results include highly detailed maps of the Bunaken reefs habitat (194 polygons mapped and a census of 175 habitats). The influence of sea level fall on coral mortality during the El-Nino event of 2015 - 2016 is presented in detail, and the importance of this process is also discussed from the interpretation of a unique time series of 15 years of VHR images. The temporal series reveals very different trajectories of the coral habitats. We conclude that Bunaken reefs demonstrate an ability to resileince and without phase shift, but that a definitive diagnosis of their resilience remains difficult to determine by imagery. Habitat trajectories can not be fully interpreted without changing some monitoring paradigms and without using a combination of remote sensing and in situ data.

Detection of structural changes in time series is a topic with increasing pop- ularity among econometricians over the last decades. The main aim of this thesis was to review and compare the classical and modern econometric meth- ods of structural change detection and unit root testing. A recent method for testing a one-time break in at most linear trend function of a series without prior knowledge about the stationary or unit root nature of the error compo- nent proposed by Perron and Yabu (2009b) was studied. Subsequently, it was combined with the unit root test that allows for a break in trend proposed by Kim and Perron (2009) to examine the nature of the error component. All the methods for change detection and unit root testing were compared in a Monte Carlo simulation study that indicated significant improvement in power of the Perron-Yabu and Kim-Perron tests against most alternatives compared to the classical methods. However, all tests demonstrated poor performance in case of a quadratic trend function. Finally, the tests were employed in a practical ex- ample to examine the properties of the quarterly GDP time series of the Czech Republic. 1

Driving a motor vehicle is a complex activity, and errors in performing the driving task can result in crashes which cause property damage, injuries, and sometimes death. It is important that the road environment supports drivers in safe performance of the driving task. At present, increasing amounts of visual information from sources such as roadside advertising create visual clutter in the road environment. There has been little research on the effect of this visual clutter on driving performance, particularly for vulnerable groups such as novice and older drivers. The present work aims to fill this gap. Literature from a variety of relevant disciplines was surveyed and integrated, and a model of the mechanisms by which visual clutter could affect performance of the driving task was developed. To determine potential sources of clutter, focus groups with drivers were held and two studies involving subjective ratings of visual clutter in photographs and video clips of road environments were carried out. This resulted in a taxonomy of visual clutter in the road environment: ‘situational clutter’, including vehicles and other road users with whom drivers interact; ‘designed clutter’, including road signs, signals, and markings used by traffic authorities to communicate with users; and ‘built clutter’, including roadside development and any signage not originating from a road authority. The taxonomy of visual clutter was tested using the change detection paradigm. Drivers were slower to detect changes in photographs of road scenes with high levels of visual clutter than with low levels, and slower for road scenes including advertising billboards than road scenes without billboards. Finally, the effects of billboard presence and lead vehicles on vehicle control, eye movements and responses to traffic signs and signals were tested using a driving simulator. The number of vehicles included appeared to be insufficient to create situational clutter. However billboards had significant effects on driver speed (slower), ability to follow directions on road signs (slower with more errors), and eye movements (increased amount of time fixating on roadsides at the expense of scanning the road ahead). Older drivers were particularly affected by visual clutter in both the change detection and simulated driving tasks. Results are discussed in terms of implications for future research and for road safety practitioners. Visual clutter can affect driver workload as well as purely visual aspects of the driving task (such as hazard perception and search for road signs). When driver workload is increased past a certain point other driving tasks will also be performed less well (such as speed maintenance). Advertising billboards in particular cause visual distraction, and should be considered at a similar level of potential danger as visual distraction from in-vehicle devices. The consequences of roadside visual clutter are more severe for the growing demographic of older drivers. Currently, road environments do not support drivers (particularly older drivers) as well as they could. Based on the results, guidance is given for road authorities to improve this status when designing and location road signage and approving roadside advertising.