# Robust Self-Ensembling Network for Hyperspectral Image Classification

arXiv: https://arxiv.org/abs/2104.03765 · HTML: https://mlsanity.com/paper/2104.03765/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 59 references that are in the map.
- First posted on arXiv 8 Apr 2021.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016)
- [Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation](https://mlsanity.com/paper/1809.09478/) (2018)
- [Swapout: Learning an ensemble of deep architectures](https://mlsanity.com/paper/1605.06465/) (2016)
- [Statistical Loss and Analysis for Deep Learning in Hyperspectral Image Classification](https://mlsanity.com/paper/1912.12385/) (2019)
- [Few-Shot Hyperspectral Image Classification With Unknown Classes Using Multitask Deep Learning](https://mlsanity.com/paper/2009.03508/) (2020)
- [Every Node Counts: Self-Ensembling Graph Convolutional Networks for Semi-Supervised Learning](https://mlsanity.com/paper/1809.09925/) (2018)
- [Naive Gabor Networks for Hyperspectral Image Classification](https://mlsanity.com/paper/1912.03991/) (2019)

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

- [One-Class Risk Estimation for One-Class Hyperspectral Image Classification](https://mlsanity.com/paper/2210.15457/) (2022)
- [Language-Informed Hyperspectral Image Synthesis for Imbalanced-Small Sample Classification via Semi-Supervised Conditional Diffusion Model](https://mlsanity.com/paper/2502.19700/) (2025)
- [HyperTTA: Test-Time Adaptation for Hyperspectral Image Classification under Distribution Shifts](https://mlsanity.com/paper/2509.08436/) (2025)
- [Multi-Teacher Multi-Objective Meta-Learning for Zero-Shot Hyperspectral Band Selection](https://mlsanity.com/paper/2406.07949/) (2024)

## Abstract (arXiv)

Recent research has shown the great potential of deep learning algorithms in the hyperspectral image (HSI) classification task. Nevertheless, training these models usually requires a large amount of labeled data. Since the collection of pixel-level annotations for HSI is laborious and time-consuming, developing algorithms that can yield good performance in the small sample size situation is of great significance. In this study, we propose a robust self-ensembling network (RSEN) to address this problem. The proposed RSEN consists of two subnetworks including a base network and an ensemble network. With the constraint of both the supervised loss from the labeled data and the unsupervised loss from the unlabeled data, the base network and the ensemble network can learn from each other, achieving the self-ensembling mechanism. To the best of our knowledge, the proposed method is the first attempt to introduce the self-ensembling technique into the HSI classification task, which provides a different view on how to utilize the unlabeled data in HSI to assist the network training. We further propose a novel consistency filter to increase the robustness of self-ensembling learning. Extensive experiments on three benchmark HSI datasets demonstrate that the proposed algorithm can yield competitive performance compared with the state-of-the-art methods. Code is available online (urlhttps://github.com/YonghaoXu/RSEN).