Mixture-Tuned Matched Filter (MTMF)

Mixture-Tuned Matched Filter (MTMF)#

MTMF is a target-detection tool: given a reference spectrum for a material of interest, it scores every pixel for how strongly that material is present, without needing spectra for any of the other (“background”) materials in the scene.

How it works#

MTMF combines a matched filter with a mixture-tuning feasibility check.

  1. Noise-whiten the scene (MNF). The cube is first run through a full MNF transform, which rotates the data so noise is uniform in all directions. The matched filter is far more reliable in this whitened space. The target spectrum is projected into the same MNF space.

  2. Matched filter. For each pixel, MTMF computes a score measuring how far the pixel lies along the direction of the target, relative to the background/noise distribution (Note that because this is done in MNF, the background distribution is the identity matrix). The score behaves like an abundance estimate: near 0 for background/noise, near 1 for a pure target pixel. This score map is the tool’s output.

  3. Mixture tuning (infeasibility). Internally MTMF also measures each pixel’s infeasibility — how much its spectrum deviates from a physically plausible mixture of background and target. A high matched-filter score is only a true detection when its infeasibility is low; this step is what suppresses the false positives that a plain matched filter produces.

Bad bands are excluded automatically, and nodata pixels become NaN. Each target produces one float32 score image named MTMF [target]: <source>; higher values indicate a stronger match.

Using the tool#

  1. Input — leave the type as Image Cube and choose the dataset to analyze. (Spectrum input is not currently supported.)

  2. Noise method — choose how background noise is estimated:

    • Image Cube Based — estimate noise from the scene itself via the shift-difference method; pick a direction (Down/Up/Left/Right) in the row below.

    • Dark Image Based — supply a separate dark/noise dataset (same bands as the input) in the value box.

    • ROI Based — supply a region of interest (ROI) and a dataset where the ROI will be pulled from

  3. Target — choose the reference spectrum to detect.

  4. Click OK. The run proceeds in the background.