Multiclass Boundary Refinement with Neural Cellular Automata for Remote Sensing Land Cover Segmentation

Key points

• We treat boundary refinement as the iterated application of a small learned local rule: the Boundary Refinement Neural Cellular Automaton (BRNCA), that starts from the class probabilities of a frozen, pre-trained segmenter and corrects them cell by cell.1
• We generalise the binary rNCA to the multiclass case by keeping the visible channels on the probability simplex through an Aitchison (log-space) update and by adding free hidden channels.1
• The best configuration improves the mean BIoU over the base segmenter by 2.36 points (from 46.63 to 48.99).1
• The gain is concentrated on the irregular woodland class (+8.5 points BIoU); this suggests that position error is correlated with the fractal dimension of the class boundaries.1

Sources (1)

• [1]Multiclass Boundary Refinement with Neural Cellular Automata for Remote Sensing Land Cover Segmentation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 04:41 PM

  Semantic segmentation of remote sensing imagery often places class boundaries a few pixels away from where they truly lie, and this position error is concentrated in the thin band measured by Boundary IoU (BIoU).

  We treat boundary refinement as the iterated application of a small learned local rule: the Boundary Refinement Neural Cellular Automaton (BRNCA), that starts from the class probabilities of a frozen, pre-trained segmenter and corrects them cell by cell.

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