Data Preprocessing Steps

Atmospheric Correction

• Purpose: Corrects satellite images for atmospheric disturbances such as haze, aerosols, and Rayleigh scattering.
• Methods: Algorithms like Sen2Cor for Sentinel-2 data use specific models to estimate and remove atmospheric effects.
• Importance: Enhances the accuracy of spectral analysis and improves the consistency of satellite data for comparative studies.

Geometric Correction

• Purpose: Adjusts satellite images to remove distortions caused by Earth's curvature and sensor position.
• Techniques: Orthorectification involves projecting images onto a standardized map coordinate system using terrain models and satellite sensor parameters.
• Benefits: Ensures spatial accuracy and alignment of images, essential for precise geographic analysis and mapping.

Resampling

• Purpose: Adjusts the spatial resolution of satellite images to match other data or analysis requirements.
• Methods: Interpolation techniques like nearest-neighbor, bilinear, or cubic convolution are used to compute new pixel values based on existing ones.
• Considerations: Trade-offs between resolution enhancement and potential loss of information or introduction of artifacts should be balanced based on specific application needs.

Image Registration

• Purpose: Aligns multiple satellite images or different sensor bands to a common coordinate system.
• Methods: Uses control points or tie points to match corresponding features between images, ensuring spatial consistency.
• Applications: Facilitates image fusion, change detection, and multi-temporal analysis by enabling accurate pixel-to-pixel comparison.

Quality Assessment

• Purpose: Evaluates the reliability and usability of satellite data for intended applications.
• Criteria: Includes parameters such as radiometric accuracy, sensor calibration status, cloud cover percentage, and image metadata completeness.
• Actions: Data with low quality indicators may be filtered out or undergo further preprocessing steps to mitigate inaccuracies.

Data Normalization

• Purpose: Adjusts the range or distribution of pixel values in satellite images to standardize data for consistent analysis.
• Techniques: Normalization methods like min-max scaling or z-score normalization are applied to enhance the comparability of different image datasets.
• Benefits: Improves the effectiveness of machine learning algorithms by reducing the influence of data variability and enhancing model training convergence.