Processing Kernel for remote sensing data
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Classify input image input.tif with a support vector machine. A training sample that is provided as an OGR vector dataset. It contains all features (same dimensionality as input.tif) in its fields (please check pkextract on how to obtain such a file from a "clean" vector file containing locations only). A two-fold cross validation (cv) is performed (output on screen). The parameters cost and gamma of the support vector machine are set to 1000 and 0.1 respectively. A colourtable (a five column text file: image value, RED, GREEN, BLUE, ALPHA) has also been provided.
Classification using bootstrap aggregation. The training sample is randomly split in three subsamples (33% of the original sample each).
Classification using prior probabilities for each class. The priors are automatically normalized. The order in which the options -p are provide should respect the alphanumeric order of the class names (class 10 comes before 2...)