Multi-satellite image alignment over large areas with featureless regions

There is a great deal of interest in fusing together the information provided by different satellites for real-time change detection on the surface of the earth. For detecting important types of changes on the ground, it is necessary to inject geometry into the data provided by low-res satellites using multi-view imaging satellites that record each point on the ground from multiple perspectives. Combining these perspectives and generating a DSM (Digital Surface Model) gives us the geometry needed for a more meaningful analysis of satellite data. Before such an analysis can be carried out, it is necessary to align the images from all available satellites. Automatic image alignment, however, requires features on the ground that can be identified and correctly matched across different images using computer vision algorithms. While such features are common in urban areas, that is not always the case in predominantly rural areas that present a more-or-less uniform texture to the sensors. In this paper we present methods for automatic identification and alignment of featureless regions. Featureless regions are identified using point spread maps, which are a byproduct of DSM generation. The subsequent strategy for aligning featureless regions depends on the proportion of featureless regions to feature-rich regions. If most of the AOI (Area of Interest) is feature-rich, we ignore featureless regions when estimating inter-satellite image alignment parameters and apply those parameters to the entire AOI. Finally, we present a technique to propagate and fuse parameters from feature-rich regions to featureless regions.