# Learning crop type mapping from regional label proportions in large-scale SAR and optical imagery

arXiv: https://arxiv.org/abs/2208.11607 · HTML: https://mlsanity.com/paper/2208.11607/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 54 references that are in the map.
- First posted on arXiv 24 Aug 2022.

## Builds on (most cited first, 21 of 21)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [Prototypical Contrastive Learning of Unsupervised Representations](https://mlsanity.com/paper/2005.04966/) (2020)
- [Self-labelling via simultaneous clustering and representation learning](https://mlsanity.com/paper/1911.05371/) (2019)
- [Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data](https://mlsanity.com/paper/2103.16607/) (2021)
- [Geography-Aware Self-Supervised Learning](https://mlsanity.com/paper/2011.09980/) (2020)
- [Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders](https://mlsanity.com/paper/1802.02080/) (2018)
- [Self-Supervised Learning of Remote Sensing Scene Representations Using Contrastive Multiview Coding](https://mlsanity.com/paper/2104.07070/) (2021)
- [On Learning from Label Proportions](https://mlsanity.com/paper/1402.5902/) (2014)
- [Deep multi-class learning from label proportions](https://mlsanity.com/paper/1905.12909/) (2019)
- [Learning from Label Proportions with Generative Adversarial Networks](https://mlsanity.com/paper/1909.02180/) (2019)
- [Differentiable Deep Clustering with Cluster Size Constraints](https://mlsanity.com/paper/1910.09036/) (2019)
- [Two-stage Training for Learning from Label Proportions](https://mlsanity.com/paper/2105.10635/) (2021)
- [Unsupervised Deep Feature Extraction for Remote Sensing Image Classification](https://mlsanity.com/paper/1511.08131/) (2015)
- [Self-supervised Pre-training with Hard Examples Improves Visual Representations](https://mlsanity.com/paper/2012.13493/) (2020)
- [Learning from Label Proportions: A Mutual Contamination Framework](https://mlsanity.com/paper/2006.07330/) (2020)
- [Large-Scale Hyperspectral Image Clustering Using Contrastive Learning](https://mlsanity.com/paper/2111.07945/) (2021)

## Built on by (most cited first, 5 of 5)

- [Self-supervised remote sensing feature learning: Learning Paradigms, Challenges, and Future Works](https://mlsanity.com/paper/2211.08129/) (2022)
- [Forming Auxiliary High-confident Instance-level Loss to Promote Learning from Label Proportions](https://mlsanity.com/paper/2411.10364/) (2024)
- [On-orbit model training for satellite imagery with label proportions](https://mlsanity.com/paper/2306.12461/) (2023)
- [FedX: Explanation-Guided Pruning for Communication-Efficient Federated Learning in Remote Sensing](https://mlsanity.com/paper/2508.06256/) (2025)
- [Multi-Label Proportion Learning for Sea-Ice Type Prediction](https://mlsanity.com/paper/2609.16347/) (2026)

## Abstract (arXiv)

The application of deep learning algorithms to Earth observation (EO) in recent years has enabled substantial progress in fields that rely on remotely sensed data. However, given the data scale in EO, creating large datasets with pixel-level annotations by experts is expensive and highly time-consuming. In this context, priors are seen as an attractive way to alleviate the burden of manual labeling when training deep learning methods for EO. For some applications, those priors are readily available. Motivated by the great success of contrastive-learning methods for self-supervised feature representation learning in many computer-vision tasks, this study proposes an online deep clustering method using crop label proportions as priors to learn a sample-level classifier based on government crop-proportion data for a whole agricultural region. We evaluate the method using two large datasets from two different agricultural regions in Brazil. Extensive experiments demonstrate that the method is robust to different data types (synthetic-aperture radar and optical images), reporting higher accuracy values considering the major crop types in the target regions. Thus, it can alleviate the burden of large-scale image annotation in EO applications.