A Model-Free Time Series Segmentation Approach for Land Cover Change Detection

author: Ashish Garg, Department of Computer Science and Engineering, University of Minnesota
produced by: NASA Ames Video and Graphics Branch
published: June 27, 2012,   recorded: October 2011,   views: 3495

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Ecosystem-related observations from remote sensors on satellites o er signi ficant possibility for understanding the location and extent of global land cover change. In this paper, we focus on time series segmentation techniques in the context of land cover change detection. We propose a model-based time series segmentation algorithm inspired by an event detection framework proposed in the field of statistics. We also present a novel model-free change detection algorithm for detecting land cover change that is computationally simple, efficient, non-parametric and takes into account the inherent variability present in the remote sensing data. A key advantage of this method is that it can be applied globally for a variety of vegetation without having to identify the right model for specifi c vegetation types. We evaluate the change detection capacity of the proposed techniques on both synthetic and MODIS EVI data sets. We illustrate the importance and relative ability of di fferent algorithms to account for the natural variation in the EVI data set.

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