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The current practice in land cover/land use change analysis relies heavily onthe individually classified maps of the multitemporal data set. Due to varyingacquisition conditions (e.g., illumination, sensors, seasonal differences), theclassification maps yielded are often inconsistent through time for robuststatistical analysis. 3D geometric features have been shown to be stable forassessing differences across the temporal data set. Therefore, in this articlewe investigate he use of a multitemporal orthophoto and digital surface modelderived from satellite data for spatiotemporal classification. Our approachconsists of two major steps: generating per-class probability distribution mapsusing the random-forest classifier with limited training samples, and makingspatiotemporal inferences using an iterative 3D spatiotemporal filter operatingon per-class probability maps. Our experimental results demonstrate that theproposed methods can consistently improve the individual classification resultsby 2%-6% and thus can be an important postclassification refinement approach.

3D Iterative Spatiotemporal Filtering for Classification of Multitemporal Satellite Data Sets