. . "201800301" . "GFM_004" . "7"^^ . "196"^^ . "10"^^ . "2023-07-06T22:00:00Z"^^ . "f2f" . "Image analysis requires a mixture of theoretical concepts and practical skills. The subjects will be introduced in lectures and applied in practical classes. As a preparation for lectures, reading textbook material will be recommended on some subjects. In addition, on some other subjects reading research articles will be recommended after the lecture to go deeper into the subject.\nPractical classes will consist of a mixture of a demo by an instructor, individual work following written instructions and summarizing the outcome of the exercise in a class. In practical class students are supposed to work with existing programming codes and modify these (to a limited degree). In this way the students can get insight in the intermediate stages of the image analysis algorithms and make decisions on the outcomes. In these summaries reflection on theoretical concepts will be done. In this way a solid integration of theory and practice will be achieved."@en . . . . . . . "Preferably subjects as covered in the courses Scientific Geocomputing, Acquisition and Exploration of Geospatial Data and the course Extraction, Analysis and Dissemination of Geospatial Information ,Knowledge and skills in programming, linear filters, basic image classification, basic photogrammetry.\n\nPreferably subjects as covered in the courses Scientific Geocomputing, Acquisition and Exploration of Geospatial Data and the course Extraction, Analysis and Dissemination of Geospatial Information."@en . . . "6"^^ . "4" . "2B" . "2023-04-23T22:00:00Z"^^ . "In this course, you will be introduced to more advanced image analysis methods enabling to enrich your geo-information problem solving abilities. Image processing methods treated in previous courses, such as linear filters, feature based DTM production and conventional hard pixel based classification, face limitations making them insufficient for reliable geo-information extraction in automatic settings. Non-linear filters will be introduced for reduction of noise while preserving the boundaries. In addition, interest operators will be introduced to detect stable structures in images that are invariant to scale and rotation transformation. Various methods for dealing with objects in images will be studied: mathematical morphology and segmentation. Fuzzy and sub-pixel classification will be introduced to deal with uncertainty and to increase the information content extracted from the imagery. For multisource classification decision trees will be introduced. To automatically detect corresponding image positions, the image matching techniques will be introduced. In particular, area-based matching and feature-based matching will be investigated in this course."@en . "Image Analysis"@en . . "Image Analysis"@en . "Image Analysis"@en . . . "Learning outcome"@en . . "Apply orthorectification to derive orthophoto."@en . "Apply orthorectification to derive orthophoto."@en .