# Generative Adversarial Networks and Conditional Random Fields for Hyperspectral Image Classification

arXiv: https://arxiv.org/abs/1905.04621 · HTML: https://mlsanity.com/paper/1905.04621/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2019.

## Built on by (most cited first, 9 of 9)

- [Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects](https://mlsanity.com/paper/2101.06116/) (2021)
- [A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models](https://mlsanity.com/paper/2404.14955/) (2024)
- [A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification](https://mlsanity.com/paper/2105.14327/) (2021)
- [Adaptive DropBlock Enhanced Generative Adversarial Networks for Hyperspectral Image Classification](https://mlsanity.com/paper/2201.08938/) (2022)
- [Multi-Fake Evolutionary Generative Adversarial Networks for Imbalance Hyperspectral Image Classification](https://mlsanity.com/paper/2111.04019/) (2021)
- [C3DG: Conditional Domain Generalization for Hyperspectral Imagery Classification with Convergence and Constrained-risk Theories](https://mlsanity.com/paper/2407.04100/) (2024)
- [A 3D 2D convolutional Neural Network Model for Hyperspectral Image Classification](https://mlsanity.com/paper/2111.10293/) (2021)
- [Weak Disambiguation for Partial Structured Output Learning](https://mlsanity.com/paper/2209.09410/) (2022)
- [Modeling sequential annotations for sequence labeling with crowds](https://mlsanity.com/paper/2209.09430/) (2022)

## Abstract (arXiv)

In this paper, we address the hyperspectral image (HSI) classification task with a generative adversarial network and conditional random field (GAN-CRF) -based framework, which integrates a semi-supervised deep learning and a probabilistic graphical model, and make three contributions. First, we design four types of convolutional and transposed convolutional layers that consider the characteristics of HSIs to help with extracting discriminative features from limited numbers of labeled HSI samples. Second, we construct semi-supervised GANs to alleviate the shortage of training samples by adding labels to them and implicitly reconstructing real HSI data distribution through adversarial training. Third, we build dense conditional random fields (CRFs) on top of the random variables that are initialized to the softmax predictions of the trained GANs and are conditioned on HSIs to refine classification maps. This semi-supervised framework leverages the merits of discriminative and generative models through a game-theoretical approach. Moreover, even though we used very small numbers of labeled training HSI samples from the two most challenging and extensively studied datasets, the experimental results demonstrated that spectral-spatial GAN-CRF (SS-GAN-CRF) models achieved top-ranking accuracy for semi-supervised HSI classification.