# Reinforcement-based frugal learning for satellite image change detection

arXiv: https://arxiv.org/abs/2203.11564 · HTML: https://mlsanity.com/paper/2203.11564/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 117 references that are in the map.
- First posted on arXiv 22 Mar 2022.

## Builds on (most cited first, 30 of 30)

- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [Self-Supervised Learning of Pretext-Invariant Representations](https://mlsanity.com/paper/1912.01991/) (2019)
- [Self-supervised Learning: Generative or Contrastive](https://mlsanity.com/paper/2006.08218/) (2020)
- [Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty](https://mlsanity.com/paper/1906.12340/) (2019)
- [Is Q-learning Provably Efficient?](https://mlsanity.com/paper/1807.03765/) (2018)
- [Time-Contrastive Networks: Self-Supervised Learning from Video](https://mlsanity.com/paper/1704.06888/) (2017)
- [S4L: Self-Supervised Semi-Supervised Learning](https://mlsanity.com/paper/1905.03670/) (2019)
- [Revisiting Self-Supervised Visual Representation Learning](https://mlsanity.com/paper/1901.09005/) (2019)
- [Learning a Deep Embedding Model for Zero-Shot Learning](https://mlsanity.com/paper/1611.05088/) (2016)
- [Semantic Autoencoder for Zero-Shot Learning](https://mlsanity.com/paper/1704.08345/) (2017)
- [CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images](https://mlsanity.com/paper/1808.01097/) (2018)
- [Generalized Zero-Shot Learning via Synthesized Examples](https://mlsanity.com/paper/1712.03878/) (2017)
- [Synthesized Classifiers for Zero-Shot Learning](https://mlsanity.com/paper/1603.00550/) (2016)
- [Self-supervised Learning of Motion Capture](https://mlsanity.com/paper/1712.01337/) (2017)
- [A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts](https://mlsanity.com/paper/1712.01381/) (2017)
- [Boosting Self-Supervised Learning via Knowledge Transfer](https://mlsanity.com/paper/1805.00385/) (2018)
- [S2Looking: A Satellite Side-Looking Dataset for Building Change Detection](https://mlsanity.com/paper/2107.09244/) (2021)
- [Zero-Shot Learning via Semantic Similarity Embedding](https://mlsanity.com/paper/1509.04767/) (2015)
- [Automated Scalable Bayesian Inference via Hilbert Coresets](https://mlsanity.com/paper/1710.05053/) (2017)
- [Preserving Semantic Relations for Zero-Shot Learning](https://mlsanity.com/paper/1803.03049/) (2018)
- [Hi-UCD: A Large-scale Dataset for Urban Semantic Change Detection in Remote Sensing Imagery](https://mlsanity.com/paper/2011.03247/) (2020)
- [Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning](https://mlsanity.com/paper/1806.04798/) (2018)
- [Learning Connectivity with Graph Convolutional Networks for Skeleton-based Action Recognition](https://mlsanity.com/paper/2112.03328/) (2021)
- [A Weakly Supervised Convolutional Network for Change Segmentation and Classification](https://mlsanity.com/paper/2011.03577/) (2020)
- [Epipolar-Guided Deep Object Matching for Scene Change Detection](https://mlsanity.com/paper/2007.15540/) (2020)
- [Procedural Synthesis of Remote Sensing Images for Robust Change Detection with Neural Networks](https://mlsanity.com/paper/1905.07877/) (2019)
- [Learning deep structured network for weakly supervised change detection](https://mlsanity.com/paper/1606.02009/) (2016)
- [Targeted change detection in remote sensing images](https://mlsanity.com/paper/1803.05482/) (2018)

## Built on by (most cited first, 1 of 1)

- [Operational Change Detection for Geographical Information: Overview and Challenges](https://mlsanity.com/paper/2503.14109/) (2025)

## Abstract (arXiv)

In this paper, we introduce a novel interactive satellite image change detection algorithm based on active learning. The proposed approach is iterative and asks the user (oracle) questions about the targeted changes and according to the oracle's responses updates change detections. We consider a probabilistic framework which assigns to each unlabeled sample a relevance measure modeling how critical is that sample when training change detection functions. These relevance measures are obtained by minimizing an objective function mixing diversity, representativity and uncertainty. These criteria when combined allow exploring different data modes and also refining change detections. To further explore the potential of this objective function, we consider a reinforcement learning approach that finds the best combination of diversity, representativity and uncertainty, through active learning iterations, leading to better generalization as corroborated through experiments in interactive satellite image change detection.