通过融合空间与光谱特征提升高光谱图像聚类精度
Structural-Spectral Graph Convolution with Evidential Edge Learning for Hyperspectral Image Clustering
- 设计结构-光谱图卷积算子，同步提取超像素的空间与光谱信息
- 提出证据引导的自适应边学习模块，优化超像素拓扑图边权重
- 在4个数据集上比最优方法提升3.15%~6.06%聚类准确率，适合遥感图像分析
高光谱图像（HSI）聚类无需标注数据即可将像素分组，是重要但具挑战性的任务。针对大规模HSI，现有方法通常依赖超像素分割，在超像素层面基于图神经网络（GNN）进行聚类。然而，现有GNN难以充分挖掘输入HSI的光谱信息，且不准确的超像素拓扑图可能导致不同类别语义在信息聚合中混淆。为此，本文首先提出结构-光谱图卷积算子（SSGCO），专用于图结构化超像素，通过协同提取空间与光谱特征提升其表征质量。其次，提出证据引导的自适应边学习（EGAEL）模块，自适应预测并优化超像素拓扑图中的边权重。将所提方法融入对比学习框架实现聚类，同步完成表征学习与聚类。实验表明，该方法在四个HSI数据集上分别较最优对比方法提升2.61%、6.06%、4.96%和3.15%的聚类准确率。代码已开源。
原文摘要 · Abstract (English)
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.
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