Papers › Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural Network
Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural Network
Xiangyong Cao, Feng Zhou, Lin Xu, Deyu Meng, Zongben Xu, John Paisley
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Hyperspectral Image Classification | Indian Pines | CNN-MRF | Overall Accuracy | 96.12% | #32 of 34 | Archive leaderboard | report |
| Hyperspectral Image Classification | Pavia University | CNN-MRF | Overall Accuracy | 96.18 | #33 of 33 | Archive leaderboard | report |
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