HNGT-Net: Hard-Negative Guided Topology Transfer for Lightweight Hyperspectral Small-Target Detection

Detecting small anomalous targets in hyperspectral imagery is challenging when neither target spectra nor spatial morphologies are known, because most unsupervised detectors still encode implicit assumptions about background statistics or target structures and therefore generalize poorly to genuinely unknown threats. This paper introduces the Hard-Negative-Guided Topology Transfer Network (HNGT-Net), a lightweight teacher–student framework that addresses this problem from two perspectives. On the representation side, a similarity graph coupling spatial adjacency with feature-space nearest neighbors characterizes the spectral–spatial topology of normal backgrounds, and a three-level consistency objective over node embeddings, structural relations, and graph-Laplacian responses transfers this topology from a frozen teacher to a compact student. On the discrimination side, hard negatives are synthesized directly on pure normal pixels through sparsity-gated projected gradient perturbation, while a response-margin constraint forces the student to score such negatives above normal samples and mitigates distribution overfitting. Experiments on four public benchmark scenes and one Salinas-derived synthetic dataset show that HNGT-Net achieves an average AUC of 0.9953 with the smallest variance among all competitors. The student branch provides 10.5-fold parameter compression relative to the teacher, while the complete teacher–student stack remains compact, supporting resource-constrained remote sensing deployment.