Fully Unsupervised Bi-Temporal Change Detection Framework for VHR SAR

Owing to the unique all-weather, day-night imaging capabilities of Synthetic Aperture Radar (SAR) imaging, the modality is advantageous in the detection of anthropogenic activity. In this paper we present a fully unsupervised change detection framework that operates on Very High Resolution (VHR) SAR image pairs to produce a binary change map, without a need for per-image parameter setting. The framework is demonstrated on a pair of Capella-2 VHR X-band imagery acquired over San Diego, USA.