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BSS-Bench: Towards Reproducible and Effective Band Selection Search
The key technology to overcome the drawbacks of hyperspectral imaging (expensive, high capture delay, and low spatial resolution) and make it widely applicable is to select only a few representative bands from hundreds of bands.
ActiveDC: Distribution Calibration for Active Finetuning
Firstly, we select samples for annotation by optimizing the distribution similarity between the subset to be selected and the entire unlabeled pool in continuous space.
One-shot neural band selection for spectral recovery
To solve this ill-posed inverse problem, most band selection methods adopt hand-crafted priors or exploit clustering or sparse regularization constraints to find most prominent bands.
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