Hyperspectral target detection based on graph sampling and aggregation network

Abstract

To comprehensively utilize the spectral information encapsulated within hyperspectral images and more effectively handle the intricate and irregular structures among pixels in complex hyperspectral data, a novel graph sampling aggregation network model is put forward for hyperspectral target detection. Notably, before this study, graph sampling aggregation networks had scarcely been employed in the realm of hyperspectral target detection. This proposed model is capable of autonomously learning the effective feature representations of nodes within the graph, thereby facilitating the extraction and processing of graph data. It achieves this by extracting feature vectors through principal component analysis to construct adjacency matrices and performing convolution operations on hyperspectral images via sparse matrix multiplication, which enables the propagation and aggregation of node features within the graph structure. Upon reconstructing the image, the target data is extracted using residuals, and target detection is accomplished by minimizing the constraint energy. The model was evaluated on seven hyperspectral image datasets, and the experimental results demonstrated that the proposed graph sampling aggregation network model could proficiently detect targets with an average detection accuracy exceeding 99 . 8%, outperforming other comparative models. Concurrently, it exhibits a remarkable adaptability to the diverse characteristics of different datasets, thus validating its high level of accuracy and robustness.