urbanlc.model.pipeline_transforms.TMTransformer

class urbanlc.model.pipeline_transforms.TMTransformer(means=[0.08163042661356694, 0.10085783731931074, 0.10701118915780652, 0.20055640287459503, 0.1701256492465244, 0.12407282351539259, 291.8235398823627, 0.0, 0.0, 0.0], stds=[0.05398084773803982, 0.05849992704495288, 0.06925856941611415, 0.08748855275238117, 0.08646376806562776, 0.07575478301131, 48.37941596949521, 1.0, 1.0, 1.0])

Input Preprocessor for Landsat data from TM sensor (Landsat 4 - 7).

Methods

calculate_statistics([root, filename_glob, ...])	Calculate mean and standard deviation statistics of the dataset.
transform(img, mask[, is_training, p_hflip, ...])	Apply transformations to the input Landsat image and land cover.