# Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural Network

arXiv: https://arxiv.org/abs/1705.00727 · HTML: https://mlsanity.com/paper/1705.00727/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2017.

## Built on by (most cited first, 16 of 16)

- [Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects](https://mlsanity.com/paper/2101.06116/) (2021)
- [HSIGene: A Foundation Model For Hyperspectral Image Generation](https://mlsanity.com/paper/2409.12470/) (2024)
- [PanFlowNet: A Flow-Based Deep Network for Pan-sharpening](https://mlsanity.com/paper/2305.07774/) (2023)
- [A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification](https://mlsanity.com/paper/2105.14327/) (2021)
- [A General Adaptive Dual-level Weighting Mechanism for Remote Sensing Pansharpening](https://mlsanity.com/paper/2503.13214/) (2025)
- [Exploring Cross-Domain Pretrained Model for Hyperspectral Image Classification](https://mlsanity.com/paper/2204.03144/) (2022)
- [Shared Manifold Learning Using a Triplet Network for Multiple Sensor Translation and Fusion with Missing Data](https://mlsanity.com/paper/2210.17311/) (2022)
- [Pan-denoising: Guided Hyperspectral Image Denoising via Weighted Represent Coefficient Total Variation](https://mlsanity.com/paper/2407.06064/) (2024)
- [Probability-based Global Cross-modal Upsampling for Pansharpening](https://mlsanity.com/paper/2303.13659/) (2023)
- [Hyperspectral Anomaly Detection Fused Unified Nonconvex Tensor Ring Factors Regularization](https://mlsanity.com/paper/2505.17881/) (2025)
- [An Introduction to Robust Graph Convolutional Networks](https://mlsanity.com/paper/2103.14807/) (2021)
- [A Universal Knowledge Embedded Contrastive Learning Framework for Hyperspectral Image Classification](https://mlsanity.com/paper/2404.01673/) (2024)
- [Decision Tree Learning with Spatial Modal Logics](https://mlsanity.com/paper/2109.08325/) (2021)
- [Projected Sliced Wasserstein Autoencoder-based Hyperspectral Images Anomaly Detection](https://mlsanity.com/paper/2112.11243/) (2021)
- [HIDFlowNet: A Flow-Based Deep Network for Hyperspectral Image Denoising](https://mlsanity.com/paper/2306.17797/) (2023)
- [Self-Supervised One-Step Diffusion Refinement for Snapshot Compressive Imaging](https://mlsanity.com/paper/2409.07417/) (2024)

## Abstract (arXiv)

This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework. First, we formulate the HSI classification problem from a Bayesian perspective. Then, we adopt a convolutional neural network (CNN) to learn the posterior class distributions using a patch-wise training strategy to better use the spatial information. Next, spatial information is further considered by placing a spatial smoothness prior on the labels. Finally, we iteratively update the CNN parameters using stochastic gradient decent (SGD) and update the class labels of all pixel vectors using an alpha-expansion min-cut-based algorithm. Compared with other state-of-the-art methods, the proposed classification method achieves better performance on one synthetic dataset and two benchmark HSI datasets in a number of experimental settings.