Teilnahme am Short Course Remote Sensing

25.03.2008: Course Outline  Day 1 (25th March)

• Representing images: image formats, vector representation, matrices, eigensystems,      finding minima and maxima
• Image statistics: random variables and vectors, probability distributions,      parameter estimation, hypothesis tests, entropy, mutual information, regression,      regularization and dual formulation

Day 2 (26th March)

• Spectral transformations: PCA, MNF, spatial correlation, noise estimation
• Supervised classification: Bayes Theorem, maximum likelihood, neural networks,      support vector machines, adaptive boosting, accuracy evaluation, linear unmixing

Day 3 (27th March)

• Unsupervised classification: cost functions, K-means, hierarchic clustering,      fuzzy clustering, expectation maximization
• Change detection: iterative PCA, kernelized PCA, iterative MAD, automatic      radiometric normalization

Day 4 (28th March)

• Object-based image analysis: segmentation and objects, object features,      feature identification, classification