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ENGE203

Signal and Transform

Syllabus

  1. Fundamentals of Signal Processing in Geomatics (7 hours)
    1. Signals and systems: Understanding signals (Spatial data) and systems used in processing them
    2. Signal representation: Techniques for representing spatial signals, such as elevation and spectral data
    3. Sampling and quantization: Methods for converting continuous geospatial data into digital formats
    4. Basic signal operations: Operations such as addition and scaling applied to spatial datasets
  2. Fourier Transform for Geospatial Analysis (7 hours)
    1. Fourier series and transform: Decomposition of spatial signals into frequency components for feature analysis
    2. Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT): Frequency analysis methods for raster data and satellite imagery
    3. Spectral analysis: Identifying frequency patterns in geospatial data for classification tasks
    4. Fourier in image processing: Enhancing and processing images using Fourier techniques
  3. Digital Filtering Techniques for Spatial Data (7 hours)
    1. Digital filters overview: Types of filters (low-pass, high-pass) used in spatial data enhancement
    2. Filter design: Creating filters to remove noise and highlight features in geospatial datasets
    3. Filter performance analysis: Evaluating the effectiveness of filters in improving data quality
    4. Applications in geomatics: Practical use of filters for edge detection and smoothing in spatial data
  4. Wavelet Transform and Multiresolution Analysis (8 hours)
    1. Wavelet transform basics: Introduction to wavelets and their application in analyzing spatial data at different scales
    2. Discrete wavelet transform (DWT): Techniques for multiresolution analysis of high-resolution satellite images
    3. Wavelet-based image processing: Enhancing features and compressing images using wavelets
    4. Multiresolution analysis: Improving the interpretation of spatial phenomena by analyzing data at various scales
  5. Time-Frequency Analysis for Geospatial Data (8 hours)
    1. Time-frequency representations: Analyzing geospatial data with varying features over time
    2. Short-time Fourier transform (STFT): Handling non-stationary spatial signals for temporal analysis
    3. Spectrogram analysis: Visualizing frequency changes in time-series geospatial data
    4. Remote sensing applications: Analyzing time-series data from remote sensing platforms
  6. Advanced Signal Processing Techniques (8 hours)
    1. Adaptive filtering: Dynamic filters adjusting to changing characteristics in geospatial data
    2. Principal component analysis (PCA): Reducing dimensionality and extracting key features from spatial data
    3. Time-series analysis: Modeling and analyzing temporal changes in geospatial datasets
    4. Signal reconstruction: Techniques for interpolating and enhancing missing or degraded data

Practicals

  1. Hands-on practice with fundamental operations on geospatial signals
  2. Analyzing spatial data using Fourier techniques
  3. Design and Test Digital Filters: Developing and applying filters to enhance spatial data
  4. Enhancing and analyzing satellite images with wavelet techniques
  5. Analyzing time-series data for dynamic geospatial changes
  6. Implementing PCA and adaptive filtering in real-world scenarios