Structural-Spectral Graph Convolution with Evidential Edge Learning for Hyperspectral Image Clustering

TL;DR

This paper introduces a novel graph convolution approach with evidential edge learning for hyperspectral image clustering, enhancing spectral-spatial feature extraction and adaptive graph refinement to improve clustering accuracy.

Contribution

It proposes a structural-spectral graph convolution operator and an evidence-guided adaptive edge learning module integrated into a contrastive framework for improved HSI clustering.

Findings

Achieved up to 6.06% higher clustering accuracy on four datasets.

Enhanced spectral and spatial feature representation.

Refined superpixel graph improves class separation.

Abstract

Hyperspectral image (HSI) clustering groups pixels into clusters without labeled data, which is an important yet challenging task. For large-scale HSIs, most methods rely on superpixel segmentation and perform superpixel-level clustering based on graph neural networks (GNNs). However, existing GNNs cannot fully exploit the spectral information of the input HSI, and the inaccurate superpixel topological graph may lead to the confusion of different class semantics during information aggregation. To address these challenges, we first propose a structural-spectral graph convolutional operator (SSGCO) tailored for graph-structured HSI superpixels to improve their representation quality through the co-extraction of spatial and spectral features. Second, we propose an evidence-guided adaptive edge learning (EGAEL) module that adaptively predicts and refines edge weights in the superpixel topological graph. We integrate the proposed method into a contrastive learning framework to achieve clustering, where representation learning and clustering are simultaneously conducted. Experiments demonstrate that the proposed method improves clustering accuracy by 2.61%, 6.06%, 4.96% and 3.15% over the best compared methods on four HSI datasets. Our code is available at https://github.com/jhqi/SSGCO-EGAEL.