Handling Missing Data and Noise

Handling Missing Data

• Issue: Satellite images may contain missing or incomplete data due to cloud cover, sensor malfunctions, or data transmission errors.
• Methods:
  • Interpolation: Use neighboring pixel values or statistical methods (e.g., mean, median) to estimate missing values.
  • Image Fusion: Integrate data from multiple sources or time points to fill gaps and improve data completeness.
  • Masking: Exclude or mask unreliable or missing data regions from analysis to prevent erroneous results.

Noise Reduction

• Types of Noise: Includes sensor noise, atmospheric interference, and electromagnetic interference.
• Noise Removal Techniques:
  • Smoothing Filters: Apply spatial filters (e.g., Gaussian, median) to suppress high-frequency noise while preserving image features.
  • Statistical Methods: Use statistical models to distinguish noise from signal and filter out noise components.
  • Wavelet Transform: Decompose images into different frequency bands to selectively denoise each band.

Importance

• Enhanced Data Quality: Improves the accuracy and reliability of satellite data for subsequent analysis and modeling.
• Impact on Analysis: Reduces the risk of erroneous conclusions and enhances the effectiveness of deep learning models trained on clean, noise-free data.