Superpixel Graph Contrastive Clustering for Hyperspectral Images

Kernekoncepter

The author proposes a Superpixel Graph Contrastive Clustering (SPGCC) method to improve hyperspectral image clustering accuracy by utilizing semantic-invariant augmentations and contrastive learning.

Resumé

The content introduces the SPGCC model for hyperspectral image clustering, emphasizing the importance of spatial and spectral features. It outlines the pre-training process, superpixel segmentation, and contrastive clustering approach. Experimental results demonstrate significant improvements in clustering accuracy compared to other methods.

Hyperspectral images are challenging to cluster due to their 3-D structure.
SPGCC utilizes hybrid CNNs for feature extraction and graph learning for superpixels.
Semantic-invariant data augmentations enhance contrastive clustering performance.
The method alternates between clustering and network optimization for improved results.
Experimental results show substantial enhancements in clustering accuracy across different datasets.

Statistik

On India Pines, our model improves the clustering accuracy from 58.79% to 67.59%.

Citater

"We design two semantic-invariant data augmentations for HSI superpixels."
"Experimental results on several HSI datasets verify the advantages of the proposed method."

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Superpixel Graph Contrastive Clustering with Semantic-Invariant Augmentations for Hyperspectral Images

by Jianhan Qi,Y... kl. arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.01799.pdf

Dybere Forespørgsler

How does SPGCC compare to traditional subspace-based methods

SPGCC outperforms traditional subspace-based methods in hyperspectral image clustering by leveraging superpixel segmentation and graph contrastive learning. Unlike traditional methods like SSC, SSSC, and S5C that divide instances into low-dimensional subspaces based on spectral features, SPGCC utilizes superpixels to capture spatial information and reduce computational complexity. By incorporating semantic-invariant augmentations and sample-level alignment with clustering-center-level contrast, SPGCC learns discriminative superpixel representations that are more suitable for clustering tasks. This approach enhances intra-class similarity and inter-class dissimilarity in the embedding space, leading to improved clustering accuracy compared to traditional subspace-based methods.

What are the potential applications of SPGCC beyond hyperspectral image clustering

Beyond hyperspectral image clustering, SPGCC has potential applications in various fields where high-dimensional data needs to be clustered efficiently. Some potential applications include:

Medical Imaging: Clustering medical images for disease diagnosis or treatment planning.
Remote Sensing: Analyzing satellite imagery for environmental monitoring or disaster response.
Natural Language Processing: Clustering text data for sentiment analysis or topic modeling.
Anomaly Detection: Identifying outliers or unusual patterns in large datasets across different domains.
Recommendation Systems: Grouping users based on their preferences for personalized recommendations.

The ability of SPGCC to extract high-order spatial and spectral features through pre-training makes it versatile for a wide range of applications beyond hyperspectral image clustering.

How can semantic-invariant augmentations be further optimized for contrastive learning

To further optimize semantic-invariant augmentations for contrastive learning in SPGCC, several strategies can be considered:

Dynamic Augmentation Strategies: Implement adaptive augmentation techniques that adjust the level of perturbation based on the difficulty of samples during training.
Augmentation Diversity: Introduce a variety of augmentation types such as rotation, translation, scaling along with pixel sampling to enhance the diversity of augmented views.
Regularization Techniques: Apply regularization methods like dropout or weight decay during training to prevent overfitting when using augmented views.
Fine-tuning Augmentations: Experiment with different combinations of augmentations specific to the dataset characteristics to find optimal settings that improve contrastive learning performance.

By exploring these optimization strategies, semantic-invariant augmentations in SPGCC can be enhanced further to boost its effectiveness in capturing meaningful representations for improved clustering outcomes across diverse datasets and domains.