Skip to content

Learnable Channel Selection

Hyperspectral cameras often deliver 60–100 bands, but only a handful carry the signal you actually care about. The Channel Selector node learns which bands to keep using a two-phase training schedule: statistical warm-up followed by gradient-based refinement.

This tutorial walks through configuring and training a channel selector, then inspecting which wavelengths survived.

Run the example:

What you'll learn

  • How two-phase training combines statistical and gradient stages.
  • Configuring K (number of channels to keep) and the temperature schedule.
  • Reading the final channel weights to recover the selected wavelengths.

When to reach for channel selection

  • You want to ship a downstream model that operates on far fewer channels than the camera delivers (smaller, faster, deployable).
  • You suspect only a subset of bands carry your signal and want the model to discover them.
  • You need a learnable, end-to-end alternative to hand-picking bands.