Fig. 1 | Scientific Reports

Fig. 1

From: Collaborative representation and confidence-driven semi-supervised learning for hyperspectral image classification

Fig. 1

Flow diagram of the proposed GCN-ARE framework. The GCN-ARE framework comprises four key modules: (1) Graph construction with normalized Laplacian, (2) Supervised GCN training, (3) Recursive region subdivision via empirical risk bounds, and (4) Dynamic classifier ensemble with Hoeffding’s inequality and high computational complexity, respectively. Therefore, constructing a classification framework that can effectively model spectral-spatial relationships and flexibly adapt to heterogeneous terrains has become a core issue to be urgently addressed in the field of hyperspectral image analysis.

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