# Hyperspectral Image Classification in the Presence of Noisy Labels

arXiv: https://arxiv.org/abs/1809.04212 · HTML: https://mlsanity.com/paper/1809.04212/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2018.

## Built on by (most cited first, 12 of 12)

- [Breaking the Dilemma of Medical Image-to-image Translation](https://mlsanity.com/paper/2110.06465/) (2021)
- [Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training](https://mlsanity.com/paper/2205.04723/) (2022)
- [Towards Robustness to Label Noise in Text Classification via Noise Modeling](https://mlsanity.com/paper/2101.11214/) (2021)
- [How to Efficiently Annotate Images for Best-Performing Deep Learning Based Segmentation Models: An Empirical Study with Weak and Noisy Annotations and Segment Anything Model](https://mlsanity.com/paper/2312.10600/) (2023)
- [Change Detection from Synthetic Aperture Radar Images via Graph-Based Knowledge Supplement Network](https://mlsanity.com/paper/2201.08954/) (2022)
- [Optical Remote Sensing Image Understanding with Weak Supervision: Concepts, Methods, and Perspectives](https://mlsanity.com/paper/2204.09120/) (2022)
- [Probabilistic Machine Learning for Noisy Labels in Earth Observation](https://mlsanity.com/paper/2504.03478/) (2025)
- [Change Detection from Synthetic Aperture Radar Images via Dual Path Denoising Network](https://mlsanity.com/paper/2203.06543/) (2022)
- [Light-weight Fine-tuning Method for Defending Adversarial Noise in Pre-trained Medical Vision-Language Models](https://mlsanity.com/paper/2407.02716/) (2024)
- [A Novel Technique for Robust Training of Deep Networks With Multisource Weak Labeled Remote Sensing Data](https://mlsanity.com/paper/2510.05760/) (2025)
- [Graph-Weighted Contrastive Learning for Semi-Supervised Hyperspectral Image Classification](https://mlsanity.com/paper/2503.15731/) (2025)
- [Private Semi-supervised Knowledge Transfer for Deep Learning from Noisy Labels](https://mlsanity.com/paper/2211.01628/) (2022)

## Abstract (arXiv)

Label information plays an important role in supervised hyperspectral image classification problem. However, current classification methods all ignore an important and inevitable problem---labels may be corrupted and collecting clean labels for training samples is difficult, and often impractical. Therefore, how to learn from the database with noisy labels is a problem of great practical importance. In this paper, we study the influence of label noise on hyperspectral image classification, and develop a random label propagation algorithm (RLPA) to cleanse the label noise. The key idea of RLPA is to exploit knowledge (e.g., the superpixel based spectral-spatial constraints) from the observed hyperspectral images and apply it to the process of label propagation. Specifically, RLPA first constructs a spectral-spatial probability transfer matrix (SSPTM) that simultaneously considers the spectral similarity and superpixel based spatial information. It then randomly chooses some training samples as "clean" samples and sets the rest as unlabeled samples, and propagates the label information from the "clean" samples to the rest unlabeled samples with the SSPTM. By repeating the random assignment (of "clean" labeled samples and unlabeled samples) and propagation, we can obtain multiple labels for each training sample. Therefore, the final propagated label can be calculated by a majority vote algorithm. Experimental studies show that RLPA can reduce the level of noisy label and demonstrates the advantages of our proposed method over four major classifiers with a significant margin---the gains in terms of the average OA, AA, Kappa are impressive, e.g., 9.18%, 9.58%, and 0.1043. The Matlab source code is available at https://github.com/junjun-jiang/RLPA