Course Weekly Lecture Plan

Week	Topic
1	Introduction, Purpose of the course ? Week Contents, ? RS I flashback, ? Digital image, why need to process images? DP levels (low, mid and high) ? DI history, pixel, image band histogram and features (mean, st dev, type etc.), cumulative histograms, contrast and brightness info from histograms ? RGB colour, ? image resolutions, ? Application with different images and histograms (application areas)
2	Digital Image Processing – Pre-processing I (Radiometric correction) ? Purpose of corrections ? Radiometric corrections o Sensor imbalance (noise(bit error), striping, dropped lines) o Atmospheric corrections (purpose, haze reduction, is correction necessary? (Yes or No); Absolute atmospheric correction (Empirical Line Calibration); Relative atmospheric correction Dark object subtraction method and Regression method) o Scene illumination and viewing geometry (Seasonal Compensation, Sun angle correction, Importance for image mosaics)
3	Digital Image Processing – Pre-processing II (Geometric correction) ? Why we need (RS images are not maps!), & Purpose! ? Distortions o Systematic distortions (predictable) o Non-systematic distortions (random) o Terrain distortions ? Why is geometry important? ? When to rectify? (Mosaicking, scene to scene comparison etc…) ? Image to map (rectification) ? Image to image (registration) ? Hybrid method ? Processing steps o Locate ground control points o Compute and test a transformation matrix (Order of transformation) o Resampling (Nearest neighbour, Bilinear interpolation, Cubic convolution) ? Disadvantages of rectification
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5
6
7
8	Image enhancement – Local operations (High pass filtering, Noise, Signal-to-Noise ratio, Noise suppression)
9	Low pass filtering + High pass filtering + Noise removal
10	Image transformations - Arithmetic operations (+, -, /, x), multi-band/image manipulation (Rationing, Subtraction, Addition, Multiplication)
11
12
13	Image Processing - Classification - Supervised and Unsupervised Classification PRACTICE + HW 5 – Supervised and Unsupervised Classification
14	Classification accuracy (Sample design, Reference data, Error matrix, OA, PA, UA, Kappa error, issues)

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