Papers › Semi-Supervised Graph Prototypical Networks for Hyperspectral Image Classification
Semi-Supervised Graph Prototypical Networks for Hyperspectral Image Classification
Bobo Xi, Jiaojiao Li, Yunsong Li, Qian Du
Graph convolutional network (GCN) is one of the most favorable semi-supervised approaches, which demonstrates encouraging performance for hyperspectral image classification (HSIC), especially under the condition of small sample sizes. In this paper, we propose a novel semi-supervised graph prototypical network (SSGPN) for high-precise HSIC. Different from prevenient GCN, we devise a prototypical layer comprising a distance-based cross-entropy (DCE) loss function and a novel temporal entropy-based regularizer (TER) in the frameworks of SSGPN. This effective layer can facilitate to generate more discriminative embedding features along with the representative prototypes to each class, so as to achieve accurate identification of various land-cover categories. Additionally, to promote computational efficiency, we present a graph normalization (G-Norm) to accelerate the convergence speed and boost the training procedure. Experimental results demonstrate that our proposed SSGPN can obtain promising performance compared with the state-of-the-art methods.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections