Band selection plays a crucial role in hyperspectral image classification by
removing redundant and noisy bands and retaining discriminative ones. However,
most existing deep learning-based methods are aimed at dealing with a specific
band selection dataset, and need to retrain parameters for new datasets, which
significantly limits their generalizability.To address this issue, a novel
multi-teacher multi-objective meta-learning network (M$^3$BS) is proposed for
zero-shot hyperspectral band selection. In M$^3$BS, a generalizable graph
convolution network (GCN) is constructed to generate dataset-agnostic base, and
extract compatible meta-knowledge from multiple band selection tasks. To
enhance the ability of meta-knowledge extraction, multiple band selection
teachers are introduced to provide diverse high-quality experiences.strategy
Finally, subsequent classification tasks are attached and jointly optimized
with multi-teacher band selection tasks through multi-objective meta-learning
in an end-to-end trainable way. Multi-objective meta-learning guarantees to
coordinate diverse optimization objectives automatically and adapt to various
datasets simultaneously. Once the optimization is accomplished, the acquired
meta-knowledge can be directly transferred to unseen datasets without any
retraining or fine-tuning. Experimental results demonstrate the effectiveness
and efficiency of our proposed method on par with state-of-the-art baselines
for zero-shot hyperspectral band selection.

多老师多目标元学习网络 (M3BS) 是一种新颖的用于零样本高光谱波段选择的方法，通过构建通用的图卷积网络 (GCN) 生成数据集无关的基础，从多个波段选择任务中提取兼容的元知识，通过多目标元学习实现与多老师波段选择任务的联合优化，无需重新训练或微调即可直接转移给未知数据集。该方法在零样本高光谱波段选择中与当前最先进的基准方法表现出有效性和效率。