Spatial Context Awareness for Unsupervised Change Detection in Optical Satellite Images

• URL: http://arxiv.org/abs/2110.02068v1
• Date: Tue, 5 Oct 2021 14:13:48 GMT
• Title: Spatial Context Awareness for Unsupervised Change Detection in Optical Satellite Images
• Authors: Lukas Kondmann, Aysim Toker, Sudipan Saha, Bernhard Sch\"olkopf, Laura Leal-Taix\'e, Xiao Xiang Zhu
• Abstract summary: We introduce Sibling Regression for Optical Change detection (SiROC) SiROC is an unsupervised method for change detection in optical satellite images with medium and high resolution. It achieves competitive performance for change detection with medium-resolution Sentinel-2 and high-resolution Planetscope imagery.
• Score: 11.018182254899859
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detecting changes on the ground in multitemporal Earth observation data is one of the key problems in remote sensing. In this paper, we introduce Sibling Regression for Optical Change detection (SiROC), an unsupervised method for change detection in optical satellite images with medium and high resolution. 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 change detection. We combine this spatial context-based change detection with ensembling over mutually exclusive neighborhoods and transitioning from pixel to object-level changes with morphological operations. SiROC achieves competitive performance for change detection 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. This makes the method especially useful in conjunction with deep-learning based methods for applications such as pseudo-labeling.

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