Résumé : La texture dispose d’un bon potentiel discriminant qui complète celui des paramètres radiométriques dans le processus de classification d’image. L’indice Compact Texture Unit (CTU) multibande, récemment mis au point par Safia et He (2014), permet d’extraire la texture sur plusieurs bandes à la fois, donc de tirer parti d’un surcroît d’informations ignorées jusqu’ici dans les analyses texturales traditionnelles : l’interdépendance entre les bandes. Toutefois, ce nouvel outil n’a pas encore été testé sur des images multisources, usage qui peut se révéler d’un grand intérêt quand on considère par exemple toute la richesse texturale que le radar peut apporter en supplément à l’optique, par combinaison de données. Cette étude permet donc de compléter la validation initiée par Safia (2014) en appliquant le CTU sur un couple d’images optique-radar. L’analyse texturale de ce jeu de données a permis de générer une image en « texture couleur ». Ces bandes texturales créées sont à nouveau combinées avec les bandes initiales de l’optique, avant d’être intégrées dans un processus de classification de l’occupation du sol sous eCognition. Le même procédé de classification (mais sans CTU) est appliqué respectivement sur : la donnée Optique, puis le Radar, et enfin la combinaison Optique-Radar. Par ailleurs le CTU généré sur l’Optique uniquement (monosource) est comparé à celui dérivant du couple Optique-Radar (multisources). L’analyse du pouvoir séparateur de ces différentes bandes à partir d’histogrammes, ainsi que l’outil matrice de confusion, permet de confronter la performance de ces différents cas de figure et paramètres utilisés. Ces éléments de comparaison présentent le CTU, et notamment le CTU multisources, comme le critère le plus discriminant ; sa présence rajoute de la variabilité dans l’image permettant ainsi une segmentation plus nette, une classification à la fois plus détaillée et plus performante. En effet, la précision passe de 0.5 avec l’image Optique à 0.74 pour l’image CTU, alors que la confusion diminue en passant de 0.30 (dans l’Optique) à 0.02 (dans le CTU). / Abstract : Texture has a good discriminating power which complements the radiometric parameters in the image classification process. The index Compact Texture Unit multiband, recently developed by Safia and He (2014), allows to extract texture from several bands at a time, so taking advantage of extra information not previously considered in the traditional textural analysis: the interdependence between bands. However, this new tool has not yet been tested on multi-source images, use that could be an interesting added-value considering, for example, all the textural richness the radar can provide in addition to optics, by combining data. This study allows to complete validation initiated by Safia (2014), by applying the CTU on an optics-radar dataset. The textural analysis of this multisource data allowed to produce a "color texture" image. These newly created textural bands are again combined with the initial optical bands before their use in a classification process of land cover in eCognition. The same classification process (but without CTU) was applied respectively to: Optics data, then Radar, finally on the Optics-Radar combination. Otherwise, the CTU generated on the optics separately (monosource) was compared to CTU arising from Optical-Radar couple (multisource). The analysis of the separating power of these different bands (radiometric and textural) with histograms, and the confusion matrix tool allows to compare the performance of these different scenarios and classification parameters. These comparators show the CTU, including the CTU multisource, as the most discriminating criterion; his presence adds variability in the image thus allowing a clearer segmentation (homogeneous and non-redundant), a classification both more detailed and more efficient. Indeed, the accuracy changes from 0.5 with the Optics image to 0.74 for the CTU image while confusion decreases from 0.30 (in Optics) to 0.02 (in the CTU).

The quest for a better way to learn and teach programming, in particular object-oriented programming, is a challenge that continues to intrigue computer science educators. Even after decades of research in learning to program, educators still search for the optimal instructional approach that will solve the `learning to program effectively' problem among introductory programming students. The aim of this study was to gain insight into, and to suggest possible explanations for, the "qualitatively different ways" in which students experience learning to program using an object-oriented programming language, and to recommend teaching and learning strategies as a result of the outcomes of the research. In order to achieve these aims, a combination of phenomenographic research methods and elements of activity theory have been employed to gain an in depth understanding of pre- and in-service teachers' learning experiences. The categories of description for the phenomenon, learning to program and the influence of the learning context have been analysed and described in detail. It is argued that understanding learning to program using Java, in order to teach programming involves more than understanding learning to program as it is normally taught in university programming courses. In addition to object-oriented concepts such as message passing, inheritance, polymorphism, delegation and overriding, it entails understanding how learning to program is reflected in the goals of instruction and in different instructional practices. Knowledge of learning to program must also be linked to knowledge of students' thinking, so that teachers have conceptions of typical trajectories of student learning, and can use this knowledge to recognize landmarks of understanding in individuals. The findings suggest relationships among students' affective appraisals of the value of learning to program, their conceptions of learning to program, their approaches to learning it, their evaluations of their performance in tests and examinations and outcomes of their actions. The relationships emerged from student descriptions of their actions and the way in which different aspects of their learning and outcomes related to one another were qualitatively described and in some cases, quantified. In particular, the tensions between prior programming knowledge of a procedural language and current learning of an object-oriented language have emerged in the study. This has implications for teaching, as this study was set against the backdrop of the change in programming language in high schools, from a procedural to an object-oriented language. / Mathematical Sciences / PhD (Maths, Science and Technology Education)

The quest for a better way to learn and teach programming, in particular object-oriented programming, is a challenge that continues to intrigue computer science educators. Even after decades of research in learning to program, educators still search for the optimal instructional approach that will solve the `learning to program effectively' problem among introductory programming students. The aim of this study was to gain insight into, and to suggest possible explanations for, the "qualitatively different ways" in which students experience learning to program using an object-oriented programming language, and to recommend teaching and learning strategies as a result of the outcomes of the research. In order to achieve these aims, a combination of phenomenographic research methods and elements of activity theory have been employed to gain an in depth understanding of pre- and in-service teachers' learning experiences. The categories of description for the phenomenon, learning to program and the influence of the learning context have been analysed and described in detail. It is argued that understanding learning to program using Java, in order to teach programming involves more than understanding learning to program as it is normally taught in university programming courses. In addition to object-oriented concepts such as message passing, inheritance, polymorphism, delegation and overriding, it entails understanding how learning to program is reflected in the goals of instruction and in different instructional practices. Knowledge of learning to program must also be linked to knowledge of students' thinking, so that teachers have conceptions of typical trajectories of student learning, and can use this knowledge to recognize landmarks of understanding in individuals. The findings suggest relationships among students' affective appraisals of the value of learning to program, their conceptions of learning to program, their approaches to learning it, their evaluations of their performance in tests and examinations and outcomes of their actions. The relationships emerged from student descriptions of their actions and the way in which different aspects of their learning and outcomes related to one another were qualitatively described and in some cases, quantified. In particular, the tensions between prior programming knowledge of a procedural language and current learning of an object-oriented language have emerged in the study. This has implications for teaching, as this study was set against the backdrop of the change in programming language in high schools, from a procedural to an object-oriented language. / Mathematical Sciences / PhD (Maths, Science and Technology Education)