# Self-Pair: Synthesizing Changes from Single Source for Object Change Detection in Remote Sensing Imagery arXiv: https://arxiv.org/abs/2212.10236 ยท HTML: https://mlsanity.com/paper/2212.10236/ ## Key facts - Cited by 19 papers in the map (data through 14 Oct 2026). - Builds on 23 of its 36 references that are in the map. - First posted on arXiv 20 Dec 2022. ## Builds on (most cited first, 23 of 23) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017) - [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016) - [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019) - [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016) - [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015) - [DOTA: A Large-scale Dataset for Object Detection in Aerial Images](https://mlsanity.com/paper/1711.10398/) (2017) - [Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation](https://mlsanity.com/paper/2012.07177/) (2020) - [FDA: Fourier Domain Adaptation for Semantic Segmentation](https://mlsanity.com/paper/2004.05498/) (2020) - [Remote Sensing Image Change Detection with Transformers](https://mlsanity.com/paper/2103.00208/) (2021) - [iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images](https://mlsanity.com/paper/1905.12886/) (2019) - [SpaceNet: A Remote Sensing Dataset and Challenge Series](https://mlsanity.com/paper/1807.01232/) (2018) - [FSDR: Frequency Space Domain Randomization for Domain Generalization](https://mlsanity.com/paper/2103.02370/) (2021) - [Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks](https://mlsanity.com/paper/1810.08468/) (2018) - [Modeling Visual Context is Key to Augmenting Object Detection Datasets](https://mlsanity.com/paper/1807.07428/) (2018) - [S2Looking: A Satellite Side-Looking Dataset for Building Change Detection](https://mlsanity.com/paper/2107.09244/) (2021) - [Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery](https://mlsanity.com/paper/2011.09766/) (2020) - [Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery](https://mlsanity.com/paper/2108.07002/) (2021) - [PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation](https://mlsanity.com/paper/2103.06564/) (2021) - [A Novel Recurrent Encoder-Decoder Structure for Large-Scale Multi-view Stereo Reconstruction from An Open Aerial Dataset](https://mlsanity.com/paper/2003.00637/) (2020) - [Unsupervised Change Detection Based on Image Reconstruction Loss](https://mlsanity.com/paper/2204.01200/) (2022) - [RDP-Net: Region Detail Preserving Network for Change Detection](https://mlsanity.com/paper/2202.09745/) (2022) - [Generative Data Augmentation for Vehicle Detection in Aerial Images](https://mlsanity.com/paper/2012.04902/) (2020) ## Built on by (most cited first, 19 of 19) - [Adapting Segment Anything Model for Change Detection in HR Remote Sensing Images](https://mlsanity.com/paper/2309.01429/) (2023) - [Show Me What and Where has Changed? Question Answering and Grounding for Remote Sensing Change Detection](https://mlsanity.com/paper/2410.23828/) (2024) - [Zero-Shot Scene Change Detection](https://mlsanity.com/paper/2406.11210/) (2024) - [MapFormer: Boosting Change Detection by Using Pre-change Information](https://mlsanity.com/paper/2303.17859/) (2023) - [ZeroSCD: Zero-Shot Street Scene Change Detection](https://mlsanity.com/paper/2409.15255/) (2024) - [The Change You Want To Detect: Semantic Change Detection In Earth Observation With Hybrid Data Generation](https://mlsanity.com/paper/2503.15683/) (2025) - [A Survey of Sample-Efficient Deep Learning for Change Detection in Remote Sensing: Tasks, Strategies, and Challenges](https://mlsanity.com/paper/2502.02835/) (2025) - [ChangeAnywhere: Sample Generation for Remote Sensing Change Detection via Semantic Latent Diffusion Model](https://mlsanity.com/paper/2404.08892/) (2024) - [Improved Flood Insights: Diffusion-Based SAR to EO Image Translation](https://mlsanity.com/paper/2307.07123/) (2023) - [CAFS: Class Adaptive Framework for Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2303.11606/) (2023) - [Prototype-oriented Unsupervised Change Detection for Disaster Management](https://mlsanity.com/paper/2310.09759/) (2023) - [Remote Sensing Change Detection via Weak Temporal Supervision](https://mlsanity.com/paper/2601.02126/) (2026) - [Revisiting Change Detection Methods for their Application to Serac Fall Time-Lapse Monitoring](https://mlsanity.com/paper/2605.28100/) (2026) - [Focus or Not: A Baseline for Anomaly Event Detection On the Open Public Places with Satellite Images](https://mlsanity.com/paper/2303.11668/) (2023) - [1st Place Solution to MultiEarth 2023 Challenge on Multimodal SAR-to-EO Image Translation](https://mlsanity.com/paper/2306.12626/) (2023) - [Synthetic Aperture Radar Image Change Detection Based on Global Dynamic Context-Aware Network](https://mlsanity.com/paper/2605.16764/) (2026) - [Learning Semantic-Robust Change Detection via Semantic-Invariant Self-Distillation](https://mlsanity.com/paper/2607.19000/) (2026) - [Real-World Knowledge-Guided Change Data Synthesis for Remote Sensing](https://mlsanity.com/paper/2608.24263/) (2026) - [Spot-the-shift: Evaluating Grounded Image Difference Captioning of Long-term Changes](https://mlsanity.com/paper/2609.10356/) (2026) ## Abstract (arXiv) For change detection in remote sensing, constructing a training dataset for deep learning models is difficult due to the requirements of bi-temporal supervision. To overcome this issue, single-temporal supervision which treats change labels as the difference of two semantic masks has been proposed. This novel method trains a change detector using two spatially unrelated images with corresponding semantic labels such as building. However, training on unpaired datasets could confuse the change detector in the case of pixels that are labeled unchanged but are visually significantly different. In order to maintain the visual similarity in unchanged area, in this paper, we emphasize that the change originates from the source image and show that manipulating the source image as an after-image is crucial to the performance of change detection. Extensive experiments demonstrate the importance of maintaining visual information between pre- and post-event images, and our method outperforms existing methods based on single-temporal supervision. code is available at https://github.com/seominseok0429/Self-Pair-for-Change-Detection.