La télédétection par Radar à Synthèse d’Ouverture (RSO) offre une opportunité unique d’enregistrer, d’analyser et de prédire l’évolution de la surface de la Terre. La dernière décennie a permis l’avènement de nombreuses missions spatiales équipées de capteurs RSO (Sentinel-1, UAVSAR, TerraSAR X, etc.), ce qui a engendré une rapide amélioration des capacités d’acquisition d’images de la surface de la Terre. Le nombre croissant d’observations permet maintenant de construire des bases de données caractérisant l’évolution temporelle d’images, augmentant considérablement l’intérêt de l’analyse de séries temporelles pour caractériser des changements qui ont lieu à une échelle globale. Cependant, le développement de nouveaux algorithmes pour traiter ces données très volumineuses est un défi qui reste à relever. Dans ce contexte, l’objectif de cette thèse consiste ainsi à proposer et à développer des méthodologies relatives à la détection de changements dans les séries d’images ROS à très haute résolution spatiale.Le traitement de ces séries pose deux problèmes notables. En premier lieu, les méthodes d’analyse statistique performantes se basent souvent sur des données multivariées caractérisant, dans le cas des images RSO, une diversité polarimétrique, interférométrique, par exemple. Lorsque cette diversité n’est pas disponible et que les images RSO sont monocanal, de nouvelles méthodologies basées sur la décomposition en ondelettes ont été développées. Celles-ci permettent d’ajouter une diversité supplémentaire spectrale et angulaire représentant le comportement physique de rétrodiffusion des diffuseurs présents la scène de l’image. Dans un second temps, l’amélioration de la résolution spatiale sur les dernières générations de capteurs engendre une augmentation de l’hétérogénéité des données obtenues. Dans ce cas, l’hypothèse gaussienne, traditionnellement considérée pour développer les méthodologies standards de détection de changements, n’est plus valide. En conséquence, des méthodologies d’estimation robuste basée sur la famille des distributions elliptiques, mieux adaptée aux données, ont été développées.L’association de ces deux aspects a montré des résultats prometteurs pour la détection de changements.Le traitement de ces séries pose deux problèmes notables. En premier lieu, les méthodes d’analyse statistique performantes se basent souvent sur des données multivariées caractérisant, dans le cas des images RSO, une diversité polarimétrique ou interférométrique, par exemple. Lorsque cette diversité n’est pas disponible et que les images RSO sont monocanal, de nouvelles méthodologies basées sur la décomposition en ondelettes ont été développées. Celles-ci permettent d’ajouter une diversité spectrale et angulaire supplémentaire représentant le comportement physique de rétrodiffusion des diffuseurs présents la scène de l’image. Dans un second temps, l’amélioration de la résolution spatiale sur les dernières générations de capteurs engendre une augmentation de l’hétérogénéité des données obtenues. Dans ce cas, l’hypothèse gaussienne, traditionnellement considérée pour développer les méthodologies standards de détection de changements, n’est plus valide. En conséquence, des méthodologies d’estimation robuste basée sur la famille des distributions elliptiques, mieux adaptée aux données, ont été développées.L’association de ces deux aspects a montré des résultats prometteurs pour la détection de changements. / Remote sensing data from Synthetic Aperture Radar (SAR) sensors offer a unique opportunity to record, to analyze, and to predict the evolution of the Earth. In the last decade, numerous satellite remote sensing missions have been launched (Sentinel-1, UAVSAR, TerraSAR X, etc.). This resulted in a dramatic improvement in the Earth image acquisition capability and accessibility. The growing number of observation systems allows now to build high temporal/spatial-resolution Earth surface images data-sets. This new scenario significantly raises the interest in time-series processing to monitor changes occurring over large areas. However, developing new algorithms to process such a huge volume of data represents a current challenge. In this context, the present thesis aims at developing methodologies for change detection in high-resolution SAR image time series.These series raise two notable challenges that have to be overcome:On the one hand, standard statistical methods rely on multivariate data to infer a result which is often superior to a monovariate approach. Such multivariate data is however not always available when it concerns SAR images. To tackle this issue, new methodologies based on wavelet decomposition theory have been developed to fetch information based on the physical behavior of the scatterers present in the scene.On the other hand, the improvement in resolution obtained from the latest generation of sensors comes with an increased heterogeneity of the data obtained. For this setup, the standard Gaussian assumption used to develop classic change detection methodologies is no longer valid. As a consequence, new robust methodologies have been developed considering the family of elliptical distributions which have been shown to better fit the observed data.The association of both aspects has shown promising results in change detection applications.

Hydrological modelling is a powerful tool for hydrologists and engineers involved in the planning and development of integrated approach for the management of water resources. With the recent advent of computational power and the growing availability of spatial data, RS and GIS technologies can augment to a great extent the conventional methods used in rainfall runoff studies; it is possible to accurately describe watershed characteristics in particularly when determining runoff response to rainfall input. The main objective of this study is to apply the potential of spaceborne SAR data for soil moisture retrieval in order to improve the spatial input parameters required for hydrological modelling. For the spatial database creation, high resolution 2 m aerial laser scanning Digital Terrain Model (DTM), soil map, and landuse map were used. Rainfall records were transformed into a runoff through hydrological parameterisation of the watershed and the river network using HEC-HMS software for rainfall runoff simulation. The Soil Conservation Services Curve Number (SCS-CN) and Soil Moisture Accounting (SMA) loss methods were selected to calculate the infiltration losses. In microwave remote sensing, the study of how the microwave interacts with the earth terrain has always been interesting in interpreting the satellite SAR images. In this research soil moisture was derived from two different types of Spaceborne SAR data; TerraSAR-X and ALOS PALSAR (L band). The developed integrated hydrological model was applied to the test site of the Gottleuba Catchment area which covers approximately 400 sqkm, located south of Pirna (Saxony, Germany). To validate the model historical precipitation data of the past ten years were performed. The validated model was further optimized using the extracted soil moisture from SAR data. The simulation results showed a reasonable match between the simulated and the observed hydrographs. Quantitatively the study concluded that based on SAR data, the model could be used as an expeditious tool of soil moisture mapping which required for hydrological modelling.

Agricultural production is becoming increasingly important as the world demand increases. On the other hand, there are several factors threatening that production such as the climate change. Therefore, monitoring and management of different parameters affecting the production are important. The current study is dedicated to two key parameters, namely agricultural land cover and soil-moisture mapping using X- and L-Band Synthetic Aperture Radar (SAR) data. Land-cover mapping plays an essential role in various applications like irrigation management, yield estimation and subsidy control. A model of multi-direction/multi-distance texture analysis on SAR data and its use for agricultural land cover classification was developed. The model is built and implemented in ESRI ArcGIS software and integrated with “R Environment”. Sets of texture measures can be calculated on a plot basis and stored in an attribute table for further classification. The classification module provides various classification approaches such as support vector machine and artificial neural network, in addition to different feature-selection methods. The model has been tested for a typical Mid-European agricultural and horticultural land use pattern south to the town of Pirna (Saxony/Germany), where the high-resolution SAR data, TerraSAR-X and ALOS/PALSAR (HH/HV) imagery, were used for land-cover mapping. The results indicate that an integrated classification using textural information of SAR data has a high potential for land-cover mapping. Moreover, the multi-dimensional SAR data approach improved the overall accuracy. Soil moisture (SM) is important for various applications such as crop-water management and hydrological modelling. The above-mentioned TerraSAR-X data were utilised for soil-moisture mapping verified by synchronous field measurements. Different speckle-reduction techniques were applied and the most representative filtered image was determined. Then the soil moisture was calculated for the mapped area using the obtained linear regression equations for each corresponding land-cover type. The results proved the efficiency of SAR data in soil-moisture mapping for bare soils and at the early growing stage of fieldcrops. / Landwirtschaftliche Produktion erlangt mit weltweit steigender Nahrungsmittelnachfrage zunehmende Bedeutung. Zahlreiche Faktoren bedrohen die landwirtschaftliche Produktion wie beispielsweise die globale Klimaveränderung einschließlich ihrer indirekten Nebenwirkungen. Somit ist das Monitoring der Produktion selbst und der wesentlichen Produktionsparameter eine zweifelsfrei wichtige Aufgabe. Die vorliegende Studie widmet sich in diesem Kontext zwei Schlüsselinformationen, der Aufnahme landwirtschaftlicher Kulturen und den Bodenfeuchteverhältnissen, jeweils unter Nutzung von Satellitenbilddaten von Radarsensoren mit Synthetischer Apertur, die im X- und L-Band operieren. Landnutzungskartierung spielt eine essentielle Rolle für zahlreiche agrarische Anwendungen; genannt seien hier nur Bewässerungsmaßnahmen, Ernteschätzung und Fördermittelkontrolle. In der vorliegenden Arbeit wurde ein Modell entwickelt, welches auf Grundlage einer Texturanalyse der genannten SAR-Daten für variable Richtungen und Distanzen eine Klassifikation landwirtschaftlicher Nutzungsformen ermöglicht. Das Modell wurde als zusätzliche Funktionalität für die ArcGIS-Software implementiert. Es bindet dabei Klassifikationsverfahren ein, die aus dem Funktionsschatz der Sprache „R“ entnommen sind. Zum Konzept: Ein Bündel von Texturparametern wird durch das vorliegende Programm auf Schlagbasis berechnet und in einer Polygonattributtabelle der landwirtschaftlichen Schläge abgelegt. Auf diese Attributtabelle greift das nachfolgend einzusetzende Klassifikationsmodul zu. Die Software erlaubt nun die Suche nach „aussagekräftigen“ Teilmengen innerhalb des umfangreichen Texturmerkmalsraumes. Im Klassifikationsprozess kann aus verschiedenen Ansätzen gewählt werden. Genannt seien „Support Vector Machine“ und künstliche neuronale Netze. Das Modell wurde für einen typischen mitteleuropäischen Untersuchungsraum mit landwirtschaftlicher und gartenbaulicher Nutzung getestet. Er liegt südlich von Pirna im Freistaat Sachsen. Zum Test lagen für den Untersuchungsraum Daten von TerraSAR-X und ALOS/PALSAR (HH/HV) aus identischen Aufnahmetagen vor. Die Untersuchungen beweisen ein hohes Potenzial der Texturinformation aus hoch aufgelösten SAR-Daten für die landwirtschaftliche Nutzungserkennung. Auch die erhöhte Dimensionalität durch die Kombination von zwei Sensoren erbrachte eine Verbesserung der Klassifikationsgüte. Kenntnisse der Bodenfeuchteverteilung sind u.a. bedeutsam für Bewässerungsanwendungen und hydrologische Modellierung. Die oben genannten SAR-Datensätze wurden auch zur Bodenfeuchteermittlung genutzt. Eine Verifikation wurde durch synchrone Feldmessungen ermöglicht. Initial musste der Radar-typische „Speckle“ in den Bildern durch Filterung verringert werden. Verschiedene Filtertechniken wurden getestet und das beste Resultat genutzt. Die Bodenfeuchtebestimmung erfolgte in Abhängigkeit vom Nutzungstyp über Regressionsanalyse. Auch die Resultate für die Bodenfeuchtebestimmung bewiesen das Nutzpotenzial der genutzten SAR-Daten für offene Ackerböden und Stadien, in denen die Kulturpflanzen noch einen geringen Bedeckungsgrad aufweisen.

Urban areas around the world are changing constantly and therefore it is necessary to update urban land cover maps regularly. Remote sensing techniques have been used to monitor changes and update land-use/land-cover information in urban areas for decades. Optical imaging systems have received most of the attention in urban studies. The development of SAR applications in urban monitoring has been accelerated with more and more advanced SAR systems operating in space.   This research investigated object-based and rule-based classification methodologies for extracting urban land-cover information from high resolution SAR data. The study area is located in the north and northwest part of the Greater Toronto Area (GTA), Ontario, Canada, which has been undergoing rapid urban growth during the past decades. Five-date RADARSAT-1 fine-beam C-HH SAR images with a spatial resolution of 10 meters were acquired during May to August in 2002. Three-date RADARSAT-2 ultra-fine-beam C-HH SAR images with a spatial resolution of 3 meters were acquired during June to September in 2008.   SAR images were pre-processed and then segmented using multi-resolution segmentation algorithm. Specific features such as geometric and texture features were selected and calculated for image objects derived from the segmentation of SAR images. Both neural network (NN) and support vector machines (SVM) were investigated for the supervised classification of image objects of RADARSAT-1 SAR images, while SVM was employed to classify image objects of RADARSAT-2 SAR images. Knowledge-based rules were developed and applied to resolve the confusion among some classes in the object-based classification results.   The classification of both RADARSAT-1 and RADARSAT-2 SAR images yielded relatively high accuracies (over 80%). SVM classifier generated better result than NN classifier for the object-based supervised classification of RADARSAT-1 SAR images. Well-designed knowledge-based rules could increase the accuracies of some classes after the object-based supervised classification. The comparison of the classification results of RADARSAT-1 and RADARSAT-2 SAR images showed that SAR images with higher resolution could reveal more details, but might produce lower classification accuracies for certain land cover classes due to the increasing complexity of the images. Overall, the classification results indicate that the proposed object-based and rule-based approaches have potential for operational urban land cover mapping from high-resolution space borne SAR images. / QC 20101209

TerraSAR-X images have been tested for agricultural fields of corn and wheat. The main purpose was to evaluate the impact of daily temperatures in crop development to optimize climate induced factors on the plant growth anomalies. The results are completed by utilizing Geographic Information Science, e.g. tools of ArcMap 10.3.1 and databases of ground truth and meteorological information. Synthetic Aperture Radar (SAR) images from German Aerospace Center (DLR) are acquired and the field survey datasets are sampled, each per month for three years (2010-2012) but only for the crop seasons (April-October). Correlation between SAR images and farmland anomalies is investigated in accordance with daily heat accumulations and a comparison of the three years’ SAR backscatter signatures is explained for corn and wheat. Finding the influence of daily temperatures on crops and hence on the TerraSAR-X backscatter is developed by Growing Degree Days (GDD) which appears to be the most suitable parameter for this purpose. Observation of GDD permits that the coolest year was 2010, either rest of the years were warmer and GDD accumulated in 2011 was higher as compared to that of 2012 in the first half of the year, however 2012 had rather more heat accumulation in the second half of the year. SAR backscatter from farmland depicts the crop development stages which depend upon the time when satellite captures data during the crop season. It varies with different development stages of crop plants. Backscatter of each development stage changes as the roughness and the moisture content (dielectric property) of the plants changes and local temperature directly impacts crop growth and hence the development stages.

Lake Vostok, der größte der über 70 subglazialen Seen in der Antarktis, ist derzeit einer der Forschungsschwerpunkte der geowissenschaftlichen Polarforschung. Der See erstreckt sich unter einer 4 000 m dicken Eisschicht auf über 250 km Länge mit einer Wassertiefe von bis zu 1 000 m. Ziel der hier vorliegenden Arbeit ist die Untersuchung des Einflusses der Gezeiten auf den Wasserstand des Sees, die eine bisher nicht betrachtete Komponente in der Zirkulation im See darstellen. Auf Grund seiner Ausdehnung ist das Gezeitenpotential an verschiedenen Punkten auf dem See nicht gleich, sondern weist differentielle Unterschiede auf. Unter der Annahme, dass sich die Seeoberfläche entlang einer Äquipotentialfläche ausrichtet, ergeben sich Gleichgewichtsgezeiten des Sees mit Amplituden von bis zu 4,6 mm für die größte ganztägige Tide K1 und 1,8 mm für die größte halbtägige Tide M2. Differenzen des Luftdruckes zwischen Nord- und Südteil des Sees rufen zusätzlich einen differentiellen inversen Barometer-Effekt hervor. Der inverse Barometer-Effekt besitzt im wesentlichen die spektralen Eigenschaften eines roten Rauschens. Die Variationen erreichen bis zu +/- 20 mm. Zum messtechnischen Nachweis derartiger Höhenänderungen an der Eisoberfläche über dem See wurden drei unterschiedliche Verfahren herangezogen. Differentielle GPS-Messungen zwischen einem Punkt auf aufliegendem Eis und einem zweiten in der südlichen Seemitte bestätigen die Modellvorstellungen und zeigen sowohl mit der Luftdruckdifferenz korrelierte Höhenänderungen als auch Höhenänderungen mit ganz- und halbtägigen Perioden. Die SAR-Interferometrie als flächenhaft arbeitende Methode zur Bestimmung von Höhenänderungen lässt den räumlichen Verlauf der Deformation erkennen. Dabei zeigt sich, dass sich die Aufsetzzone auf dem etwa 50 km breiten See bis in die Seemitte ersteckt. Erdgezeitenregistrierungen, die im Jahr 1969 in der Station Vostok durchgeführt wurden, zeigen zwar Auffälligkeiten wie etwa einen stark erhöhten Luftdruckregressionskoeffzienten und einen Phasenvorlauf der K1-Tide, diese können jedoch nicht eindeutig als Resultat von Höhenänderungen der Seeoberfläche identifiziert werden. Auf Grund der Lage der Station Vostok nahe dem Ufer des Sees ist die Deformation dort schon stark gedämpft. Die zu erwartenden Effekte liegen daher unterhalb der Auflösung der damaligen Messungen. Damit sind die theoretischen Grundvorstellungen über die Reaktion des subglazialen Sees auf Gezeiten- und Luftdruckanregungen herausgearbeitet, sowie diese Effekte mit zwei unabhängigen und komplementären Messverfahren nachgewiesen. / Lake Vostok, the largest of more than 70 subglacial lakes in the Antarctic, is one of the prominent topics of recent geoscientific polar research. The lake extends beneath the 4,000 m thick ice sheet to a length of more than 250 km with a water depth of up to 1,000 m. This thesis aims to investigate the influence of tides on the lake level which has not been considered so far in the discussion of water circulation within the lake. Due to the extent of the lake the tidal potential at different positions on its surface is not equal but exhibits a differential effect. Under the assumption of the lake level to be parallel to an equipotential surface the equilibrium tides of the lake yield amplitudes of up to 4.6 mm for the largest diurnal tidal constituent K1 and 1.8 mm for the largest semi-diurnal wave M2. In addition, differences in air pressure between the northern and the southern part of the lake result in a differential inverse barometric effect. This effect shows red noise characteristics with variations of up to +/- 20 mm. Three different types of measurements were used to verify corresponding height changes of the ice surface above the lake. Differential GPS measurements between one station on grounded ice and one in the southern centre of the lake confirm the concept and show height changes correlated to air pressure differences as well as changes with diurnal and semi-diurnal periods. SAR interferometry as a spatial method to determine height changes reveals the areal extent of the deformation with a grounding zone extending to the centre of the about 50 km wide lake. Gravimetric earth tide data recorded at Vostok Station in 1969 show pecularities such as an increased regression with air pressure and a phase lead of the K1 tide. However, these effects cannot be explicitly attributed to height changes of the lake surface. Due to the position of the station near the edge of the lake the effect is highly attenuated and below the noise level of these measurements. This work introduces the concept of the response of the subglacial lake to the tidal potential and to air pressure forcings and presents evidence for the effect by two different techniques proving the validity of the model.

Additiv tillverkning (AM) är en växande tillverkningsteknologi som har många lovande tekniska, ekologiska och ekonomiska aspekter. Selektiv lasersmältning (SLM) är den AM-metod som står i framkanten av den utveckling som sker inom teknologin. SLM har kapabiliteten att tillverka detaljer med jämförbart goda mekaniska egenskaper gentemot konventionella tillverkningsmetoder men drabbas av vanligt förekommande defekter som hämmar dess möjligheter att bli en mer använd bearbetningsmetod i tillverkningsindustrin. I detta arbete, som tar an formen av en State of the Art Rapport, presenteras SLM-metoden på en teknisk nivå, den jämförs med andra AM-metoder samt med konventionell tillverkning, flera metaller och legeringar som finns tillgängliga för bearbetning presenteras och dess senaste utvecklingar samt framtid presenteras och diskuteras. / Additive manufacturing (AM) is a growing manufacturing technology which has many promising technical, ecological, and economical aspects. Selective Laser Melting (SLM) is the AM-method which stands on the forefront of the development which is taking place in this technology. SLM has the capability to produce components with relatively good mechanical characteristics as compared to conventional manufacturing methods. However, the method is suffering from common defects which inhibits its chances to become a more widely-used method in the manufacturing industry. In this work, which takes on the form of a State of the Art Report, the SLM-method is presented on a technical level. It is then put in comparison to other AM-methods and conventional manufacturing as a whole. Some of the metals and alloys available for SLM are listed. The latest developments in SLM are presented and lastly, the future developments of SLM is discussed.

[pt] O estudo dos eventos naturais e dos gerados pela atividade humana no mar tem tido uma grande prioridade para o setor de petróleo, isso devido à possibilidade de ter um evento perigoso para o ambiente marinho ou a área de produção. Nesse contexto, o objetivo deste trabalho é a avaliação de abordagens baseadas em aprendizado profundo para a classificação de eventos no mar usando imagens de radar de abertura sintética na área de óleo e gás. Métodos baseados em aprendizado profundo têm mostrado um ótimo desempenho através do uso de camadas convolucionais, onde as características são extraídas automaticamente através da definição de um kernel e stride. As seguintes arquiteturas são avaliadas neste trabalho: Inception V3, Xception, Inception ResNet V2, MobileNet, VGG16 e Deep Attention sampling. A avaliação é feita em uma metodologia de classificação de eventos no mar usando duas bases de dados de imagens de radar: a primeira contém 10 eventos comumente presentes no oceano ártico, e a segunda descreve um derramamento de óleo presente na costa da Louisiana. Nos experimentos realizados se obteve os melhores resultados com as arquiteturas Deep Attention sampling as quais atingiram valores de f1-score e Recall de até 0.82 por cento e 0.87 por cento respectivamente, para a classe de interesse no conjunto de dados de derramamento de óleo. Para o conjuntode dados de eventos naturais no mar, um alto desempenho foi evidenciado para arquiteturas baseadas no uso de módulos de Inception, tendo pontuações mais altas de F1-score e Recall para a arquitetura Xception. Além disso, foi observado uma melhoria de até 10 por cento e 13 por cento nas métricas f1-score e Recall no uso da atenção, em relação à sua arquitetura base (VGG16), e 4 por cento respeito a outras arquiteturas baseadas em módulos Inception, isto para o conjunto de dados de eventos no mar, demonstrando as vantagens de usar amostragem com atenção. / [en] The study of natural events and those generated by human activity at sea has been a high priority for the Oil and Gas industry, due to the possibility of a dangerous event for the marine environment or the production area. In this context, the objective of this work is the evaluation of approaches based on deep learning for the classification of events in the sea using synthetic aperture radar images in the oil and gas area. Methods based on deep learning have shown an excellent performance through the use of convolutional layers, where the characteristics are extracted automatically through the definition of a kernel and stride. The following architectures are evaluated in this work: Inception V3, Xception, Inception ResNet V2, MobileNet, VGG16, and Deep Attention sampling. The assessment is made using a methodology for classifying events at sea using two radar image databases: the first contains 10 events commonly present in the Arctic Ocean, and the second describes an oil spill present near the Louisiana coast. In the experiments performed, the best results were obtained with the Deep Attention sampling architectures, which reached f1- score and Recall values of up to 0.82 a per cent nd 0.87 per cent respectively, for the class of interest in the oil spill dataset. For the dataset of natural events in the sea, high performance was evidenced for architectures based on the non-use of Inception modules, having higher values of F1-score and Recall for an Xception architecture. Also, an improvement of up to 10 per cent and 13 per cent in the metrics f1- score and recall in the use of attention was observed, concerning its base architecture (VGG16), and 4 per cent with other architectures based on Inception modules, this for the dataset of events at sea, demonstrating the advantages of using Attention Sampling carefully.

Unmanned Aerial Vehicles (UAVs) could play an important role in Search & Rescue (SAR) operations thanks to their ability to cover large, remote, or inaccessible search areas quickly without putting any personnel at risk. As UAVs are becoming autonomous, the problem of identifying humans in a variety of conditions can be solved with computer vision implemented with a thermal camera. In some cases, it would be necessary to operate with one or several small, agile UAVs to search for people in dense and narrow environments, where flying at a high altitude is not a viable option. This could for example be in a forest, cave, or a collapsed building. A small UAV has a limitation in carrying capacity, which is why this thesis aimed to propose a lightweight thermal solution for human detection that could be applied on a small SAR-UAV for operation in dense environments. The solution included a Raspberry Pi 4 and a FLIR Lepton 3.5 thermal camera in terms of hardware, which were mainly chosen thanks to their small footprint regarding size and weight, while also fitting within budget restrictions. In terms of object detection software, EfficentDet-Lite0 in TensorFlow Lite format was incorporated thanks to good balance between speed, accuracy, and resource usage. An own dataset of thermal images was collected and trained upon. The objective was to characterize disturbances and challenges this solution might face during a UAV SAR-operation in dense environments, as well as to measure how the performance of the proposed platform varied with increasing amount of environmental coverage of a human. This was solved by conducting a literature study, an experiment in a replicated dense environment and through observations of the system behavior combined with analysis of the measurements. Disturbances that affect a thermal camera in use for human detection were found to be a mixture of objective and subjective parameters, which formed a base of what type of phenomena to include in a diverse thermal dataset. The results from the experiment showed that stable and reliable detection performance can be expected up to 75% vegetational coverage of a human. When fully covered, the solution was not reliable when trained on the dataset used in this thesis. / Obemannade drönare (UAVs) kan spela en viktig roll i sök- och räddningsuppdrag (SAR) tack vare deras förmåga att snabbt täcka stora, avlägsna eller otillgängliga sökområden utan att utsätta personal för risker. För autonoma UAVs kan problemet med att identifiera människor i en mängd olika förhållanden lösas med datorseende implementerat tillsammans med en värmekamera. I vissa fall kan det vara nödvändigt att operera med en eller flera små, smidiga UAVs för att söka efter människor i täta och trånga miljöer, där flygning på hög höjd inte är ett genomförbart alternativ. Det kan till exempel vara i en skog, grotta eller i en kollapsad byggnad. En liten UAV har begränsad bärförmåga, vilket är varför denna avhandling syftade till att föreslå en lättviktslösning för mänsklig detektering med värmekamera som skulle kunna appliceras på en liten SAR-UAV för drift i täta miljöer. Lösningen inkluderade Raspberry Pi 4 och en FLIR Lepton 3.5 värmekamera gällande hårdvara, tack vare liten formfaktor och liten vikt, samtidigt som de passade inom budgetramen. Gällande detekterings-mjukvara användes EfficentDet-Lite0 i TensorFlow Lite-format tack vare en bra balans mellan hastighet, noggrannhet och resursanvändning. En egen uppsättning av värmebilder samlades in och tränades på. Målet var att identifiera vilka störningar och utmaningar som denna lösning kan påträffa under en sökoperation med UAVs i täta miljöer, samt att mäta hur prestandan för den föreslagna plattformen varierade när täckningsgraden av en människa ökar p.g.a. omgivningen. Detta löstes genom att genomföra en litteraturstudie, ett experiment i en replikerad tät miljö och genom observationer av systemets beteende kombinerat med analys av mätningarna. Störningar som påverkar en värmekamera som används för mänsklig detektion visade sig vara en blandning av objektiva och subjektiva parametrar, vilka utgjorde en bas för vilka typer av fenomen som skulle inkluderas i en mångsidig kollektion med värmebilder. Resultaten från experimentet visade att stabil och pålitlig detekteringsprestanda kan förväntas upp till 75% täckningsgrad av en människa p.g.a. vegetation. När människan var helt täckt var lösningen inte tillförlitlig när den var tränad på kollektionen som användes i denna avhandling.

In synthetic aperture radar (SAR) and inverse synthetic aperture radar (ISAR), an imaging radar emits electromagnetic waves of varying frequencies towards a target and the backscattered waves are collected. By either moving the radar antenna or rotating the target and combining the collected waves, a much longer synthetic aperture can be created. These radar measurements can be used to determine the radar cross-section (RCS) of the target and to reconstruct an estimate of the target. However, the reconstructed images will suffer from spectral leakage effects and are limited in resolution. Many methods of enhancing the images exist and some are based on deep learning. Most commonly the deep learning methods rely on high-resolution ground truth data of the scene to train a neural network to enhance the radar images. In this thesis, a method that does not rely on any high-resolution ground truth data is applied to train a convolutional neural network to enhance radar images. The network takes a conventional ISAR image subject to spectral leakage effects as input and outputs an enhanced ISAR image which contains much more defined features. New RCS measurements are created from the enhanced ISAR image and the network is trained to minimise the difference between the original RCS measurements and the new RCS measurements. A sparsity constraint is added to ensure that the proposed enhanced ISAR image is sparse. The synthetic training data consists of scenes containing point scatterers that are either individual or grouped together to form shapes. The scenes are used to create synthetic radar measurements which are then used to reconstruct ISAR images of the scenes. The network is tested using both synthetic data and measurement data from a cylinder and two aeroplane models. The network manages to minimise spectral leakage and increase the resolution of the ISAR images created from both synthetic and measured RCSs, especially on measured data from target models which have similar features to the synthetic training data.  The contributions of this thesis work are firstly a convolutional neural network that enhances ISAR images affected by spectral leakage. The neural network handles complex-valued signals as a single channel and does not perform any rescaling of the input. Secondly, it is shown that it is sufficient to calculate the new RCS for much fewer frequency samples and angular positions and compare those measurements to the corresponding frequency samples and angular positions in the original RCS to train the neural network.