Infopaket

Digital image processing and decoding

ВБ 1.6.

Обов’язкова дисципліна для ОП

First

2021/2022

7 Semester

6

1. Levels of processing of remote sensing materials PR05 PR14; Algorithms for correcting the reflective characteristics of objects caused by atmospheric phenomena PR01; Geometric models: affine transformation, rubber sheet method, polynomial transformation. Resampling and its methods: interpolation methods: nearest neighbor method, etc. Methods of image transformation PR01 PR13. 2. Filtering as a method of spatial transformation. Synthesizing color images. Synergy of images and the method of principal components PR01 PR08 PR13. Creation of index images PR01 PR08. Approaches to image recognition. The essence of supervised and unsupervised classification PL02 PL05 PL08. 3. Algorithms for image transformation. Developing students' ability to apply knowledge to select and process space images to solve a specific problem PLO 02 PLO 05 PLO 08 PLO 14. 4. The ability to work autonomously with remote sensing data and obtain objective results of their processing PR02 PR05 PR13 PR14.

Full-time form

1.Students should know the basics of remote sensing, in particular, the physical basis of electromagnetic radiation, the nature and characteristics of optical radiation, properties and characteristics of images, image classification. 2.Be able to search and download satellite images from various Internet sources. Visually identify objects on large-scale images based on decoding features.

"Digital Image Processing and Decoding is a course focused on mastering the theoretical foundations and modern methods of remote sensing data processing. It provides basic information on methods of improving the spectral and spatial characteristics of images. Methods of automated interpretation - algorithms for controlled and uncontrolled image classification. The course consists of two content modules. The first content module covers the issues of pre-processing of images to improve their spectral characteristics. The content of the levels of remote sensing image processing is revealed. The second module is devoted to the issues of image processing to improve their spatial characteristics. The essence of supervised and unsupervised classification; formation of a training sample; image transformation algorithms: spectral angle method, minimum distance method, parallelepiped method, maximum likelihood method, Mahalanobis distance method. Binary coding. ISODATA method. K-means method.

1. Multispectral methods of remote sensing of the Earth in the problems of nature management. Edited by V.I. Lyalka, 2006. 2. Bilous V.V., Bodnar S.P., Kurach T.M., Molochko A.M., Patychenko G.O., Pidlisetska I.O. Remote sensing with the basics of photogrammetry: a textbook. Kyiv: Kyiv University Publishing and Printing Center, 2011. 367 p. 3. Burshtynska Kh.V. Aerospace imaging systems: a textbook, Lviv, 2010. 292 p. 4. Kokhan S.S., Vostokov A.B., Leontiev O.O. Remote sensing of the Earth. 2010. -300 с. 5. V.M. Serdyukov, G.A. Patychenko, D.A. Sinelnikov. Aerospace methods of geographical research. - K.: Main publishing house, Higher school, 1987. - 223 p.

Lecture, laboratory work, module control work, lab. work, individual independent work, test

The level of achievement of all planned learning outcomes is determined by the results of laboratory, independent, module tests and examinations. - semester assessment: 1. Module control work 1: PH 1.1-1.4, 2.1, 2.2 - 10 points. 2. Module control work 2: PH 1.5-1.6, 2.3 - 10 points. 3. Laboratory work: PH 1.4 - 4.1 - 30 points. 4. Independent work: PH 1.1 - 1.6 - 10 points. - Final assessment: in the form of a test  The final grade for the educational component, the final form of control for which is credit, is defined as the sum of grades (points) for all successfully assessed learning outcomes of 60 points and 40 points. Grades below the minimum threshold level are not added to the final grade.  It is mandatory to complete all laboratory works and module tests to obtain a positive final grade (60 points and above and "passed").

Ukrainian