Students through lectures acquire knowledge about the following topics:
Overview and definition of remote sensing. Features of the physical fields that are used in remote sensing. Sensors and systems for recording, the impact of platforms and environments.
Usable characteristics of sensors. Electro - optical digital matrix cameras, line scanner, thermal cameras, multi-spectral cameras, hyperspectral scanner. Spatial resolution, modulation
transfer function, the minimum discriminable contrast, the minimum resolved temperature difference, calibration. Synthetic aperture radar, interferometric and polarimetric mode, usable features. Improving of images. Enhencement, ranking and reduce the amount of features. The method of principal components. Unsupervised classification. Supervised classification.
Evaluation of the classification results. Registration and geocoding. Joining of images. Using of softwers for remote sensing in geoscience. Analysis and evaluation of interpretation results. Confusion matrix.
Students through practical work on exercies neet to acquire proficiency in the following skills:
Using of softwer tools (TNTlite, ImageJ, MiltiSpec) for remote sensing. Improving the images. Geometric transformations,
joining of images, geocoding. Feature enhencement. Segmentation. Transformation of images in principal components (PCA).
Unsupervised and supervised classification. Interpretation of multispectral images (visible, infrared, thermal). Interpretation of hyperspectral and radar imagesknow and distinguish the features of physical fields which were base of remote sensing, characteristics of remote sensing
features in different wavelength regions (multi-spectral, radar, hyperspectral, thermal), principles, methods and technology of the recording, interpretations
- apply knowledge and understanding of the scene based on multisensor recordings, data processing and interpretation by
addressing selected problems within the independent assignments in the remote sensing
- applying initial skills for interpretation of multisensor, multispectral and hyperspectral images
- independently drawing the conclusions about the quality and reliability of interpretation
- publicly present selected problem and its solution through the example from remote sensing
- identify areas, methods and techniques where necessary lifelong learning
- used independently one of leading software tool for remote sensing.
