AI 中文总结
该研究针对遥感系统的OOD检测难题，提出带“同意-不同意”目标的脉冲伪集成方法，破解多样性崩溃问题，以低部署成本实现了优于深度集成的检测性能。
AI 中文摘要
脉冲神经网络（SNN）适用于资源受限的遥感系统，但可靠的分布外（OOD）检测仍具挑战性。深度集成可提供强预测不确定性，但需要多个完整模型及骨干网络评估。我们提出一种高效脉冲伪集成，其将多个轻量分类头附加至冻结的SNN骨干网络。直接用交叉熵训练这些头会导致多样性崩溃，即独立参数化的头可能产生相关预测。为解决此问题，我们引入“同意-不同意”目标，该目标在保留干净分布内样本的正确预测的同时，鼓励同一输入经结构化、诱导不确定性变换后的多样性，无需外部OOD数据即可提供促进多样性的训练信号。在EuroSAT上用Spikformer和ResNet19-SNN开展的实验显示，其较常规训练的伪集成有持续改进；在UCM和AID上，使用三个骨干网络各配五个头的设置，与五模型深度集成相当或更优，同时所需参数约减少38%，骨干网络评估次数减少约40%。这些结果表明，显式促进多样性可在显著降低部署成本的同时恢复有用的集成式不确定性。
英文摘要
Spiking Neural Networks (SNNs) are attractive for resource-constrained remote-sensing systems, but reliable out-of-distribution (OOD) detection remains challenging. Deep ensembles provide strong predictive uncertainty, yet require multiple complete models and backbone evaluations. We propose an efficient spiking pseudo-ensemble that attaches multiple lightweight classification heads to a frozen SNN backbone. Naively training these heads with cross-entropy can lead to diversity collapse, where independently parameterized heads may produce correlated predictions. To address this, we introduce an agree--disagree objective that preserves correct predictions on clean in-distribution samples while encouraging diversity on structured, uncertainty-inducing transformations of the same inputs. This provides a diversity-promoting training signal without requiring external OOD data. Experiments with Spikformer and ResNet19-SNN on EuroSAT demonstrate consistent improvements over conventionally trained pseudo-ensembles. Using three backbones with five heads each matches or improves upon a five-model deep ensemble on UCM and AID, while requiring approximately 38% fewer parameters and 40% fewer backbone evaluations. These results show that explicit diversity promotion can recover useful ensemble-style uncertainty at substantially lower deployment cost.
