Spatio-Temporal Segmentation Based on Subsequences of Satellite Image Time Series

Abstract

Hierarchical image segmentation methodologies have the potential to integrate temporal information, spatial context and the hierarchical complexity of satellite image time series. The current methods, however, fail to identify the distinction between subsequences of time series, which can be essential for the interpretation of ecosystem processes. Therefore a novel conceptual methodology is introduced that allows an enhanced multi-temporal hierarchical image segmentation (EMTHIS) based on subsequences of time series (i.e., time series over a specified time window). The effect of using these subsequence windows is illustrated in an accuracy assessment approach that determines the accuracy of a classification based on subsequence windows versus the existent methodologies that do not take subsequence windows into account. Analysis of the accuracy assessment approach demonstrated the importance of considering image time series subsequences when the percentage of pixels that shows a land cover / land use change between consecutive years is above 0.5%.