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A survey of active learning algorithms for supervised remote sensing image classification

arXiv 2104.07784 ↗First posted 15 Apr 2021cs.CVcited by 27

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Abstract (arXiv)

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Builds on 0 of its 59 references that are in the map. First posted on arXiv 15 Apr 2021.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 8; 2023: 5; 2024: 4; 2025: 4; 2026: 4.

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  1. 2022A Comparative Survey of Deep Active Learningcited by 88
  2. 2022Transformers in Remote Sensing: A Surveycited by 61
  3. 2024A Survey on Deep Active Learning: Recent Advances and New Frontierscited by 53
  4. 2023Better, Not Just More: Data-Centric Machine Learning for Earth Observationcited by 19
  5. 2023OpenForest: A data catalogue for machine learning in forest monitoringcited by 15
  6. 2022Scalable First-Order Bayesian Optimization via Structured Automatic Differentiationcited by 10
  7. 2021Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite Imagescited by 9
  8. 2022Aerial Scene Parsing: From Tile-level Scene Classification to Pixel-wise Semantic Labelingcited by 8
  9. 2022Active Diffusion and VCA-Assisted Image Segmentation of Hyperspectral Imagescited by 7
  10. 2023Toulouse Hyperspectral Data Set: a benchmark data set to assess semi-supervised spectral representation learning and pixel-wise classification techniquescited by 7
  11. 2022Optical Remote Sensing Image Understanding with Weak Supervision: Concepts, Methods, and Perspectivescited by 6
  12. 2024Improving Interpretability of Deep Active Learning for Flood Inundation Mapping Through Class Ambiguity Indices Using Multi-spectral Satellite Imagerycited by 6
15 more
  1. 2025Sharpe Ratio-Guided Active Learning for Preference Optimization in RLHFcited by 5
  2. 2022ForestEyes Project: Conception, Enhancements, and Challengescited by 4
  3. 2022Was that so hard? Estimating human classification difficultycited by 3
  4. 2023Active Learning in Physics: From 101, to Progress, and Perspectivecited by 2
  5. 2024Scale-Aware Recognition in Satellite Images under Resource Constraintscited by 2
  6. 2021Reinforcement Learning Approach to Active Learning for Image Classificationcited by 1
  7. 2025Spatial-Spectral Diffusion Contrastive Representation Network for Hyperspectral Image Classificationcited by 1
  8. 2023PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classificationcited by 0
  9. 2024regAL: Python Package for Active Learning of Regression Problemscited by 0
  10. 2025CWSSNet: Hyperspectral Image Classification Enhanced by Wavelet Domain Convolutioncited by 0
  11. 2025Hierarchical Semi-Supervised Active Learning for Remote Sensingcited by 0
  12. 2026FunnelAL: Retrieve-then-Rank Active Learning for Single-Class Discoverycited by 0
  13. 2026Fermat Active Laplace Learning for Semi-Supervised Hyperspectral Image Classificationcited by 0
  14. 2026A Remote Approach to Cashew Orchard Detection: Leveraging Active Learning with Satellite Imagery in Guinea-Bissaucited by 0
  15. 2026Label-Efficient Learning for Ground-Based Sky-Image Classification: A Benchmark of Transfer Learning, Active Learning, and Pseudo-Labeling on GCDcited by 0