# Spatial Context Awareness for Unsupervised Change Detection in Optical Satellite Images

arXiv: https://arxiv.org/abs/2110.02068 · HTML: https://mlsanity.com/paper/2110.02068/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 58 references that are in the map.
- First posted on arXiv 5 Oct 2021.

## Builds on (most cited first, 4 of 4)

- [Deep learning in remote sensing: a review](https://mlsanity.com/paper/1710.03959/) (2017)
- [Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks](https://mlsanity.com/paper/1810.08468/) (2018)
- [Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery](https://mlsanity.com/paper/1803.02642/) (2018)
- [Physically constrained causal noise models for high-contrast imaging of exoplanets](https://mlsanity.com/paper/2010.05591/) (2020)

## Built on by (most cited first, 8 of 8)

- [DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation](https://mlsanity.com/paper/2203.12560/) (2022)
- [Fuzzy clustering for the within-season estimation of cotton phenology](https://mlsanity.com/paper/2211.14099/) (2022)
- [SpaceYOLO: A Human-Inspired Model for Real-time, On-board Spacecraft Feature Detection](https://mlsanity.com/paper/2302.00824/) (2023)
- [UCDFormer: Unsupervised Change Detection Using a Transformer-driven Image Translation](https://mlsanity.com/paper/2308.01146/) (2023)
- [S2C: Learning Noise-Resistant Differences for Unsupervised Change Detection in Multimodal Remote Sensing Images](https://mlsanity.com/paper/2502.12604/) (2025)
- [Big Earth Data and Machine Learning for Sustainable and Resilient Agriculture](https://mlsanity.com/paper/2211.12584/) (2022)
- [Exploiting Neighborhood Structural Features for Change Detection](https://mlsanity.com/paper/2302.05114/) (2023)
- [Bridging Supervision Gaps: A Unified Framework for Remote Sensing Change Detection](https://mlsanity.com/paper/2601.17747/) (2026)

## Abstract (arXiv)

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.