Spatial Context Awareness for Unsupervised Change Detection in Optical Satellite Images

Abstract

Detecting changes on the ground in multitemporal Earth observation data is one of the key problems in remote sensing. In this article, we introduce Sibling Regression for Optical Change detection (SiROC), an unsupervised method for change detection (CD) in optical satellite images with medium and high resolutions. SiROC is a spatial context-based method that models a pixel as a linear combination of its distant neighbors. It uses this model to analyze differences in the pixel and its spatial context-based predictions in subsequent time periods for CD. We combine this spatial context-based CD with ensembling over mutually exclusive neighborhoods and transitioning from pixel to object-level changes with morphological operations. SiROC achieves competitive performance for CD with medium-resolution Sentinel-2 and high-resolution Planetscope imagery on four datasets. Besides accurate predictions without the need for training, SiROC also provides a well-calibrated uncertainty of its predictions.