Concetti Chiave
The author proposes a Superpixel Graph Contrastive Clustering (SPGCC) method to improve hyperspectral image clustering accuracy by utilizing semantic-invariant augmentations and contrastive learning.
Sintesi
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.
Statistiche
On India Pines, our model improves the clustering accuracy from 58.79% to 67.59%.
Citazioni
"We design two semantic-invariant data augmentations for HSI superpixels."
"Experimental results on several HSI datasets verify the advantages of the proposed method."