Spatio-Temporal Segmentation Applied to Optical Remote Sensing Image Time Series

The availability of a large amount of remote sensing data made Earth Observation increasingly accessible and detailed. High temporal and spatial resolution sensors are responsible for making available data sets of time series in unprecedented proportions. Within this context, the use of efficient segmentation algorithms of remote sensing imagery represents an important role in this scenario, because they provide homogeneous regions in space-time and hence simplify the data set. In addition, the spatio-temporal segmentation can bring a new way of interpreting data by means of analyzing contiguous regions in time. This letter describes a method for image segmentation applied to time series of the Earth Observation data. We adapted the traditional region growing method to detect homogeneous regions in space and time. Study cases were conducted by considering the dynamic time warping algorithm as the homogeneity criterion to grow regions. Tests on high temporal resolution image sequences from Moderate Resolution Imaging Spectroradiometer and Landsat-8 Operational Land Imager vegetation indices and comparisons with other distance measurements provided satisfactory outcomes.