Hyperspectral image classification using feature fusion fuzzy graph broad network

• Published in: Information sciences Vol. 689; p. 121504
• Main Authors: Chu, Yonghe, Cao, Jun, Ding, Weiping, Huang, Jiashuang, Ju, Hengrong, Cao, Heling, Liu, Guangen
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.01.2025
• Subjects: Feature fusion; Fuzzy set; Graph convolutional network; Hyperspectral image; Inter-class graph; Intra-class graph; Intra-class graph; Graph convolutional network; Inter-class graph; Feature fusion; Hyperspectral image; Fuzzy set
• ISSN: 0020-0255
• DOI: 10.1016/j.ins.2024.121504

In recent years, graph convolutional networks (GCNs) have shown strong performance in hyperspectral image (HSI) classification. However, traditional GCN methods often use superpixel-based nodes to reduce computational complexity, which fails to capture pixel-level spectral-spatial features. Additionally, these methods typically focus on matching predicted labels with ground truth, neglecting the relationships between inter-class and intra-class distances, leading to less discriminative features. To address these issues, we propose a feature fusion fuzzy graph broad network (F3GBN) for HSI classification. Our method extracts pixel-level attribute contour features using attribute filters and fuses them with superpixel features through canonical correlation analysis. We employ a broad learning system (BLS) as the classifier, which fully utilizes spectral-spatial information via nonlinear transformations. Furthermore, we construct intra-class and inter-class graphs based on fuzzy set and manifold learning theories to ensure better clustering of samples within the same class and separation between different classes. A novel loss function is introduced in BLS to minimize intra-class distances and maximize inter-class distances, enhancing feature discriminability. The proposed F3GBN model achieved impressive overall accuracy on public datasets: 96.73% on Indian Pines, 98.29% on Pavia University, 98.69% on Salinas, and 99.43% on Kennedy Space Center, outperforming several classical and state-of-the-art methods, thereby demonstrating its effectiveness and feasibility. •Pixel and superpixel feature fusion using canonical correlation enhances representation.•Broad learning system classifier fully utilizes spectral-spatial information.•New loss function improves accuracy by optimizing intra-class and inter-class feature distances.