  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.
