Multi-sensor anomalous change detection at scale

Combining multiple satellite remote sensing sources provides a far richer, more frequent view of the earth than that of any single source; the challenge is in distilling these petabytes of heterogeneous sensor imagery into meaningful characterizations of the imaged areas. To meet this challenge requires effective algorithms for combining heterogeneous data to identify subtle but important changes among the intrinsic data variation. The major obstacle to using heterogeneous satellite data to monitor anomalous changes across time is this: subtle but real changes on the ground can be overwhelmed by artifacts that are simply due to the change in modality. Here, we implement a joint-distribution framework for anomalous change detection that can effectively "normalize" for these changes in modality, and does not require any phenomenological resampling of the pixel signal. This flexibility enables the use of satellite imagery from different sensor platforms and modalities. We use multi-year construction of the Los Angeles Stadium at Hollywood Park (in Inglewood, CA) as our testbed, and exploit synthetic aperture radar (SAR) imagery from Sentinel-1 and multispectral imagery from both Sentinel-2 and Landsat 8. We explore results for anomalous change detection between Sentinel-2 and Landsat 8 over time, and also show results for anomalous change detection between Sentinel-1 SAR imagery and Sentinel-2 multispectral imagery.