1. Digital image data from Remote sensing. Paradoxes of digital images, principles of segmentation.
2. Review of physical properties, spectral characteristics of landscape objects and phenomena and identification methods .
3. Preprocessing of digital images. Rectification methods. Radiometric and atmospherical corrections. Radiometric errors in data and its elimination. 5S model, Modtran, ATCOR 1-4, Sen2Cor, reflectivity calculation on the Earth surface, calculation of surface temperature.
4. Image enhancement methods. Thresholding, contrast modification, density slices, colour synthesis. Image filtration. Convolution. Filter separability. Low frequency filters, directional smoothing.
5. High frequency filters, edge operators, Laplacian operators, Canny detector, edge detection, pattern detection, edge delocalisation.
6. Texture, local textural measures (Haralick function), image segmentation methods (based on thresholding, edge detection, region based, hybrid methods), detection of geometric features, Hough transformation.
7. Object-based image analysis (OBIA). Utilization of segmentation, methods of delimitation of image objects, algorithms Baatz-Shäpe. Fourier transformation.
8. Spectral indeces. Variants of vegetation indeces, indeces for mineral detection, humidity, snow and others. Data fusion for images with different spatial resolution
9. Pixel-based clasification. Supervised spectral clasification of multispectral images. A training phase, correction of training sites. Parametric and non-parametric classifiers.
10. Pixel-based unsupervised classification methods. K-means, ISODATA, ISOCLUSTER. Hybrid clasification. Neural networks and advanced techniques of classification (deep learning, convolutional neural network).
11. Soft classifiers, utlization of Bayes theorem, Dempster-Shafer theory, classification and uncertainty. Assessment of classification results. Post-classification modification.
12. Image spectroscopy, hyperspectral and ultraspectral data. A review of sensors. Preprocessing and atmospherical corrections, flat field conversion, empirical line, models, PCA, MNF. Pixel purity index. End members and their discovering. Classification of hyperspectral data. Applications.
13. Processing data from radar systemms. Principles, biasesFactors influencing resulting signal. Coregistration of a pair of SAR products, interferogram creating and estimation of coherence, removal of demarcation from interferogram, image filtering. DInSAR method. Radar polarimetry. LIDAR and UAV. Processing of lidar data. Application fields.
2. Review of physical properties, spectral characteristics of landscape objects and phenomena and identification methods .
3. Preprocessing of digital images. Rectification methods. Radiometric and atmospherical corrections. Radiometric errors in data and its elimination. 5S model, Modtran, ATCOR 1-4, Sen2Cor, reflectivity calculation on the Earth surface, calculation of surface temperature.
4. Image enhancement methods. Thresholding, contrast modification, density slices, colour synthesis. Image filtration. Convolution. Filter separability. Low frequency filters, directional smoothing.
5. High frequency filters, edge operators, Laplacian operators, Canny detector, edge detection, pattern detection, edge delocalisation.
6. Texture, local textural measures (Haralick function), image segmentation methods (based on thresholding, edge detection, region based, hybrid methods), detection of geometric features, Hough transformation.
7. Object-based image analysis (OBIA). Utilization of segmentation, methods of delimitation of image objects, algorithms Baatz-Shäpe. Fourier transformation.
8. Spectral indeces. Variants of vegetation indeces, indeces for mineral detection, humidity, snow and others. Data fusion for images with different spatial resolution
9. Pixel-based clasification. Supervised spectral clasification of multispectral images. A training phase, correction of training sites. Parametric and non-parametric classifiers.
10. Pixel-based unsupervised classification methods. K-means, ISODATA, ISOCLUSTER. Hybrid clasification. Neural networks and advanced techniques of classification (deep learning, convolutional neural network).
11. Soft classifiers, utlization of Bayes theorem, Dempster-Shafer theory, classification and uncertainty. Assessment of classification results. Post-classification modification.
12. Image spectroscopy, hyperspectral and ultraspectral data. A review of sensors. Preprocessing and atmospherical corrections, flat field conversion, empirical line, models, PCA, MNF. Pixel purity index. End members and their discovering. Classification of hyperspectral data. Applications.
13. Processing data from radar systemms. Principles, biasesFactors influencing resulting signal. Coregistration of a pair of SAR products, interferogram creating and estimation of coherence, removal of demarcation from interferogram, image filtering. DInSAR method. Radar polarimetry. LIDAR and UAV. Processing of lidar data. Application fields.