- Raster data gathered using remote sensing methods. N-dimensional image data. Elementary descriptive statistics. Spatial statistics for remote sensing. Multiple linear regression.
- Image data format, import and export. Image file format conversion. Remote sensing image data overview.
- Digital image data errors. Image data pre-processing. Radiometric and atmospheric image correction.
- Image enhancement . The main aim of image enhancement techniques and its review.
- Radiometric, spatial, spectral enhancement of remote sensed data. Multispectral image processing.
- Image geometric transformation. Image registration and the removal of geometric distortion. Numeric transformation, polynomial equations, ground control points, transformation matrix. Image moving, scale, rotation, resampling.
- Extracting information from image. Visual interpretation and automated classification. Main approaches to image classification. Classification rules.
- Using spectral classification rules in supervised or unsupervised process. Parametric and non-parametric classification rules. Evaluating of automatomated image classification. Comparing semi-automated classification and visual interpretation methods.
- Complementary classification approaches for image processing. Contextual classification, Automated Change Detection and Classification. Fuzzy image classification. Using artificial intelligence. Object-oriented image classification.
- Hyperspectral image processing.
- Radar sensed image processing.
- Integration of remote sensed data with GIS.
- Image data format, import and export. Image file format conversion. Remote sensing image data overview.
- Digital image data errors. Image data pre-processing. Radiometric and atmospheric image correction.
- Image enhancement . The main aim of image enhancement techniques and its review.
- Radiometric, spatial, spectral enhancement of remote sensed data. Multispectral image processing.
- Image geometric transformation. Image registration and the removal of geometric distortion. Numeric transformation, polynomial equations, ground control points, transformation matrix. Image moving, scale, rotation, resampling.
- Extracting information from image. Visual interpretation and automated classification. Main approaches to image classification. Classification rules.
- Using spectral classification rules in supervised or unsupervised process. Parametric and non-parametric classification rules. Evaluating of automatomated image classification. Comparing semi-automated classification and visual interpretation methods.
- Complementary classification approaches for image processing. Contextual classification, Automated Change Detection and Classification. Fuzzy image classification. Using artificial intelligence. Object-oriented image classification.
- Hyperspectral image processing.
- Radar sensed image processing.
- Integration of remote sensed data with GIS.