Infopaket

Automatic Interpretation of Remote Sensing Data

ВК 1.01

Вибіркова дисципліна для ОП

Second

2023/2024

1 Semester

3

- To know the theoretical foundations of geology, geodesy, higher and engineering geodesy, topographic and thematic mapping, compilation and updating of maps, remote sensing of the Earth and photogrammetry, land management, real estate evaluation and land cadastre. - Know the methods of collecting and structuring information for scientific research in the field of geoinformatics, photogrammetry and DZS, processing methods digital images in the environments of special packages, programs and GIS. - Apply methods and technologies for creating state geodetic networks and special engineering geodetic networks, topographic surveying terrain, topographic and geodetic measurements for research, design, construction and operation of engineering structures, public, industrial and agricultural complexes using modern ground and aerospace methods.

possession of elementary skills of working with a personal computer.

Within the framework of the educational discipline, modern trends in the application of remote sensing data for a wide range of nature protection, nature use, monitoring tasks, etc. are considered. A list of modern application software (both commercial and open) that is actively used for standard and advanced processing of remote sensing data is given. The theoretical foundations of some common algorithms for the classification of multidimensional data are studied, and practical work involving these algorithms in solving specific applied problems is also carried out. Students acquire the theoretical knowledge, practical skills and abilities necessary for the objective statement of the problem, its formalization, the selection of input data, the classification algorithm and the necessary software environment, which provides the possibility of carrying out the appropriate thematic deciphering and interpretation of the obtained 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. Krasovskyi H. Ya. Kosmichnyi monitorynh bezpeky vodnykh ekosystem z zastosuvanniam heoinformatsiinykh tekhnolohii. Kyiv, Naukova Dumka, 2007. 4. Miao Li, Shuying Zang, Bing Zhang, Shanshan Li & Changshan Wu. A Review of Remote Sensing Image Classification Techniques: the Role of Spatio-contextual Information. European Journal of Remote Sensing Vol. 47 , Iss. 1,2014. http://dx.doi.org/10.5721/EuJRS20144723 5. Pattern recognition, fourth edition / Sergios Theodoridis, Konstantinos Koutroumbas. – Elsevier Inc., 2009. – 961 p.

Lectures, practical classes, independent work

Control is carried out according to the modular rating system and includes: performance of 5 practical works (where students have to demonstrate the quality of acquired knowledge and solve the tasks using the methods and tools outlined by the teacher), and conducting 1 written modular control work. The final assessment is conducted in the form of an exam

ukrainian