A Scalable Probabilistic Change Detection Algorithm for Very High Resolution (VHR) Satellite Imagery

Detecting landscape changes using very high-resolution multispectral imagery demands an accurate and scalable algorithm that is robust to geometric and atmospheric errors. Existing pixel-based change detection approaches, however, have several drawbacks, which render them ineffective for VHR imagery analysis. A recent probabilistic change detection framework provides more accurate assessment of changes than traditional approaches by analyzing image patches than pixels. However, this patch (grid)-based approach produces coarse-resolution (patch size) changes. In this work we present a sliding window based approach that produces changes at the native image resolution. The increased computational demand of the sliding window based approach is addressed through thread-level parallelization on shared memory architectures. Our experimental evaluation showed a 91% performance improvement compared to its sequential counterpart on a sq. KM aerial image with varying window sizes on a 16-core (32 virtual threads) Intel Xeon processor.