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

arXiv: https://arxiv.org/abs/2104.07784 · HTML: https://mlsanity.com/paper/2104.07784/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 59 references that are in the map.
- First posted on arXiv 15 Apr 2021.

## Built on by (most cited first, 27 of 27)

- [A Comparative Survey of Deep Active Learning](https://mlsanity.com/paper/2203.13450/) (2022)
- [Transformers in Remote Sensing: A Survey](https://mlsanity.com/paper/2209.01206/) (2022)
- [A Survey on Deep Active Learning: Recent Advances and New Frontiers](https://mlsanity.com/paper/2405.00334/) (2024)
- [Better, Not Just More: Data-Centric Machine Learning for Earth Observation](https://mlsanity.com/paper/2312.05327/) (2023)
- [OpenForest: A data catalogue for machine learning in forest monitoring](https://mlsanity.com/paper/2311.00277/) (2023)
- [Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation](https://mlsanity.com/paper/2206.08366/) (2022)
- [Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite Images](https://mlsanity.com/paper/2110.07782/) (2021)
- [Aerial Scene Parsing: From Tile-level Scene Classification to Pixel-wise Semantic Labeling](https://mlsanity.com/paper/2201.01953/) (2022)
- [Toulouse Hyperspectral Data Set: a benchmark data set to assess semi-supervised spectral representation learning and pixel-wise classification techniques](https://mlsanity.com/paper/2311.08863/) (2023)
- [Active Diffusion and VCA-Assisted Image Segmentation of Hyperspectral Images](https://mlsanity.com/paper/2204.06298/) (2022)
- [Optical Remote Sensing Image Understanding with Weak Supervision: Concepts, Methods, and Perspectives](https://mlsanity.com/paper/2204.09120/) (2022)
- [Improving Interpretability of Deep Active Learning for Flood Inundation Mapping Through Class Ambiguity Indices Using Multi-spectral Satellite Imagery](https://mlsanity.com/paper/2404.19043/) (2024)
- [Sharpe Ratio-Guided Active Learning for Preference Optimization in RLHF](https://mlsanity.com/paper/2503.22137/) (2025)
- [ForestEyes Project: Conception, Enhancements, and Challenges](https://mlsanity.com/paper/2208.11687/) (2022)
- [Was that so hard? Estimating human classification difficulty](https://mlsanity.com/paper/2203.11824/) (2022)
- [Active Learning in Physics: From 101, to Progress, and Perspective](https://mlsanity.com/paper/2307.03899/) (2023)
- [Scale-Aware Recognition in Satellite Images under Resource Constraints](https://mlsanity.com/paper/2411.00210/) (2024)
- [Reinforcement Learning Approach to Active Learning for Image Classification](https://mlsanity.com/paper/2108.05595/) (2021)
- [Spatial-Spectral Diffusion Contrastive Representation Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2502.19699/) (2025)
- [PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification](https://mlsanity.com/paper/2306.16918/) (2023)
- [regAL: Python Package for Active Learning of Regression Problems](https://mlsanity.com/paper/2410.17917/) (2024)
- [CWSSNet: Hyperspectral Image Classification Enhanced by Wavelet Domain Convolution](https://mlsanity.com/paper/2509.09163/) (2025)
- [Hierarchical Semi-Supervised Active Learning for Remote Sensing](https://mlsanity.com/paper/2511.18058/) (2025)
- [FunnelAL: Retrieve-then-Rank Active Learning for Single-Class Discovery](https://mlsanity.com/paper/2607.25276/) (2026)
- [Fermat Active Laplace Learning for Semi-Supervised Hyperspectral Image Classification](https://mlsanity.com/paper/2608.02483/) (2026)
- [A Remote Approach to Cashew Orchard Detection: Leveraging Active Learning with Satellite Imagery in Guinea-Bissau](https://mlsanity.com/paper/2608.11996/) (2026)
- [Label-Efficient Learning for Ground-Based Sky-Image Classification: A Benchmark of Transfer Learning, Active Learning, and Pseudo-Labeling on GCD](https://mlsanity.com/paper/2609.26631/) (2026)

## 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.