Multitemporal Fusion for the Detection of Static Spatial Patterns in Multispectral Satellite Images, with Application to Archaeological Survey

Citation:

Bjoern H. Menze and Jason A. Ur. 2014. “Multitemporal Fusion for the Detection of Static Spatial Patterns in Multispectral Satellite Images, with Application to Archaeological Survey.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS)IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS), 7, Pp. 3513-3524. Publisher's Version Copy at https://j.mp/2mQEKOY

Abstract:

We evaluate and further develop a multitemporal fusion strategy that we use to detect the location of ancient settlement sites in the Near East and to map their distribution, a spatial pattern that remains static over time. For each ASTER images that has been acquired in our survey area in northeastern Syria, we use a pattern classification strategy to map locations with a multispectral signal similar to the one from (few) known archaeological sites nearby. We obtain maps indicating the presence of anthrosol-soils that formed in the location of ancient settlements and that have a distinct spectral pattern under certain environmental conditions-and find that pooling the probability maps from all available time points reduces the variance of the spatial anthrosol pattern significantly. Removing biased classification maps-i.e., those that rank last when comparing the probability maps with the (limited) ground truth we have- reduces the overall prediction error even further, and we estimate optimal weights for each image using a nonnegative least squares regression strategy. The ranking and pooling strategy approach we propose in this study shows a significant improvement over the plain averaging of anthrosol probability maps that we used in an earlier attempt to map archaeological sites in a 20 000-km 2 area in northern Mesopotamia, and we expect it to work well in other surveying tasks that aim in mapping static surface patterns with limited ground truth in long series of multispectral images.
Last updated on 07/29/2018