An Unsupervised Approach for Subpixelic Land-Cover Change Detection

Abstract : In this paper, we present a new method for subpixelic land-cover change detection using coarse resolution time series, as they offer a high time-repetitiveness of acquisition. Changes are detected by analyzing the coherence between a coarse resolution time series and a high resolution classification as a description of the land-cover state at the date of reference. To that aim, an a contrario model is derived, leading to the definition of a probabilistic coherence criterion free of parameter and free of any a priori information. This measure is the core of a stochastic algorithm that selects automatically the image sub-domain representing the most likely changes. Some particular problems related to the use of time series are discussed, such as the potential high variability of a time series or the problem of missing data. Some experiments are then presented on pseudo-actual data, showing a good performance for change detection and a high robustness to the considered resolution ratio (between the high resolution classification and the coarse resolution time series).
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Submitted on : Monday, September 19, 2011 - 1:32:05 AM
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Amandine Robin, Lionel Moisan, Sylvie Le Hégarat-Mascle. An Unsupervised Approach for Subpixelic Land-Cover Change Detection. International Workshop on the Analysis of Multi-temporal Remote Sensing Images (MultiTemp), 2007, Belgium. pp.1-6, ⟨10.1109/MULTITEMP.2007.4293061⟩. ⟨hal-00624502⟩



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