mlsanityData to 7 Oct 2026

Self-Pair: Synthesizing Changes from Single Source for Object Change Detection in Remote Sensing Imagery

arXiv 2212.10236 ↗First posted 20 Dec 2022cs.CVcited by 19

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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.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 23 of its 36 references that are in the map. First posted on arXiv 20 Dec 2022.

Builds on

23 of 36 references.

References · 23

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11 more
  1. 2021FSDR: Frequency Space Domain Randomization for Domain Generalizationcited by 163
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 7; 2024: 4; 2025: 2; 2026: 6.

19 papers.

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  1. 2023Adapting Segment Anything Model for Change Detection in HR Remote Sensing Imagescited by 56
  2. 2024Show Me What and Where has Changed? Question Answering and Grounding for Remote Sensing Change Detectioncited by 15
  3. 2024Zero-Shot Scene Change Detectioncited by 13
  4. 2023MapFormer: Boosting Change Detection by Using Pre-change Informationcited by 12
  5. 2024ZeroSCD: Zero-Shot Street Scene Change Detectioncited by 12
  6. 2025The Change You Want To Detect: Semantic Change Detection In Earth Observation With Hybrid Data Generationcited by 12
  7. 2025A Survey of Sample-Efficient Deep Learning for Change Detection in Remote Sensing: Tasks, Strategies, and Challengescited by 9
  8. 2024ChangeAnywhere: Sample Generation for Remote Sensing Change Detection via Semantic Latent Diffusion Modelcited by 8
  9. 2023Improved Flood Insights: Diffusion-Based SAR to EO Image Translationcited by 5
  10. 2023CAFS: Class Adaptive Framework for Semi-Supervised Semantic Segmentationcited by 4
  11. 2023Prototype-oriented Unsupervised Change Detection for Disaster Managementcited by 1
  12. 2026Remote Sensing Change Detection via Weak Temporal Supervisioncited by 1
7 more
  1. 2026Revisiting Change Detection Methods for their Application to Serac Fall Time-Lapse Monitoringcited by 1
  2. 2023Focus or Not: A Baseline for Anomaly Event Detection On the Open Public Places with Satellite Imagescited by 0
  3. 20231st Place Solution to MultiEarth 2023 Challenge on Multimodal SAR-to-EO Image Translationcited by 0
  4. 2026Synthetic Aperture Radar Image Change Detection Based on Global Dynamic Context-Aware Networkcited by 0
  5. 2026Learning Semantic-Robust Change Detection via Semantic-Invariant Self-Distillationcited by 0
  6. 2026Real-World Knowledge-Guided Change Data Synthesis for Remote Sensingcited by 0
  7. 2026Spot-the-shift: Evaluating Grounded Image Difference Captioning of Long-term Changescited by 0