{'fraction_non_alphanumeric': 0.018260869565217393, 'fraction_numerical': 0.008695652173913044, 'mean_word_length': 4.994791666666667, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Efficient Satellite Image Time Series Analysis Under Time Warping

Earth observation satellites are now providing images with short revisit cycle and high spatial resolution. The amount of produced data requires new methods that will give a sound temporal analysis while being computationally efficient. Dynamic time warping has proved to be a very sound measure to capture similarities in radiometric evolutions. In this letter, we show that its nonlinear distortion behavior is compatible with the use of a spatiotemporal segmentation of the data cube that is formed by a satellite image time series (SITS). While dealing with spatial and temporal dimensions of SITS at the same time had already proven to be very challenging, this letter proves that, by taking advantage of the spatial and temporal connectivities, both the performance and the quality of the analysis can be improved. Our method is assessed on a SITS of 46 Formosat -2 images sensed in 2006, with an average cloud cover of one third. We show that our approach induces the following: 1) sharply reduced memory usage; 2) improved classification results; and 3) shorter running time.