Infopaket

Automatic Interpretation of Remote Sensing Data

ВК 2.1.1.3

Вибіркова дисципліна для ОП

Second

2023/2024

3 Semester

4

Analyze the features of natural and anthropogenic systems and objects of the upper part of the earth's crust and its sedimentary layer in particular, apply their knowledge to identify and solve problematic issues and make informed decisions in the thematic processing of geological, geophysical and other geospatial data. Be able to communicate with specialists and experts of different levels of other fields of knowledge, including in the international context, in the global information environment To be able to carry out thematic processing and interpretation of geospatial data obtained by various methods of studying the geological environment, to develop appropriate algorithms and software products, to form geodatabases, to create web publications of cartographic data. Create specialized software products, use web technologies and remote sensing data in scientific and industrial activities.

1. Successful completion of the course "Remote sensing of the Earth". 2. Successful completion of the courses "Linear Algebra and Analytical Geometry", "Probability Theory and Mathematical Statistics". Probability theory and mathematical statistics". 3. Successful completion of the courses "Topography", "Geodesy". 4. Possession of elementary skills in working with a personal computer.

The discipline examines current trends in the use of remote sensing data for a wide range of environmental, natural resource management, monitoring tasks, etc. A list of modern application software (both commercial and open source) that is actively used for standard and advanced processing of remote sensing data is provided. The theoretical foundations of some common algorithms for classifying multidimensional data are studied, and practical work is performed with the involvement of these algorithms in solving specific applied problems. Students acquire the theoretical knowledge, practical skills and abilities necessary for the substantive formulation of the problem, its formalization, and selection of input data, classification algorithm and the necessary software environment that provides the ability to conduct appropriate thematic decryption and interpretation of the results.

1. Robert A. Schowengerdt. Remote Sensing: Models and Methods for Image Processing. Third Edition. Elsevier, 2007. 2. Jon Atli Benediktsson, Pedram Ghamisi. Spectral-Spatial Classification of Hyperspectral Remote Sensing Images. Artech House, 2015 3. Jerome O Connell, Ute Bradter, Tim G. Benton. Using high resolution CIR imagery in the classification of non-cropped areas in agricultural landscapes in the UK. Remote Sensing for Agriculture, Ecosystems, and Hydrology XV, edited by Christopher M. U. Neale, Antonino Maltese. Proc. of SPIE Vol. 8887, 888708 · © 2013 SPIE. doi: 10.1117/12.2028356 4. Jaromir Borzuchowski and Karsten Schulz. Retrieval of Leaf Area Index (LAI) and Soil Water Content (WC) Using Hyperspectral Remote Sensing under Controlled Glass House Conditions for Spring Barley and Sugar Beet. Remote Sensing 2010, 2, 1702-1721; doi:10.3390/rs2071702

Classroom classes (29%), of which: lectures - 19%, practical classes - 10%. Independent work (71%).

The control is carried out according to the module-rating system and includes: completion of 5 practical works (where students have to demonstrate the quality of the acquired knowledge and solve the tasks using the methods and tools outlined by the teacher), 10 independent practical works (where students have to demonstrate the quality of the acquired knowledge and solve the tasks set tasks without limiting the tools and techniques for solving the problem) and 2 written module tests. written module tests. The final assessment is carried out in the form of exam.

ukrainian