# Self Supervised Learning for Few Shot Hyperspectral Image Classification

arXiv: https://arxiv.org/abs/2206.12117 · HTML: https://mlsanity.com/paper/2206.12117/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 9 references that are in the map.
- First posted on arXiv 24 Jun 2022.

## Builds on (most cited first, 5 of 5)

- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [Tile2Vec: Unsupervised representation learning for spatially distributed data](https://mlsanity.com/paper/1805.02855/) (2018)
- [Deep Learning for Classification of Hyperspectral Data: A Comparative Review](https://mlsanity.com/paper/1904.10674/) (2019)
- [Large-Scale Hyperspectral Image Clustering Using Contrastive Learning](https://mlsanity.com/paper/2111.07945/) (2021)

## Built on by (most cited first, 4 of 4)

- [Self-supervised Learning in Remote Sensing: A Review](https://mlsanity.com/paper/2206.13188/) (2022)
- [PixelDINO: Semi-Supervised Semantic Segmentation for Detecting Permafrost Disturbances](https://mlsanity.com/paper/2401.09271/) (2024)
- [Contrastive Learning for Regression on Hyperspectral Data](https://mlsanity.com/paper/2403.17014/) (2024)
- [Semi-Supervised Learning for hyperspectral images by non parametrically predicting view assignment](https://mlsanity.com/paper/2306.10955/) (2023)

## Abstract (arXiv)

Deep learning has proven to be a very effective approach for Hyperspectral Image (HSI) classification. However, deep neural networks require large annotated datasets to generalize well. This limits the applicability of deep learning for HSI classification, where manually labelling thousands of pixels for every scene is impractical. In this paper, we propose to leverage Self Supervised Learning (SSL) for HSI classification. We show that by pre-training an encoder on unlabeled pixels using Barlow-Twins, a state-of-the-art SSL algorithm, we can obtain accurate models with a handful of labels. Experimental results demonstrate that this approach significantly outperforms vanilla supervised learning.