Pascoassis Souza Santos Meira, Gilson Antonio Giraldi, Rafael Duarte Coelho dos Santos · 2026 · arXiv
DOI: 10.48550/arXiv.2610.30007 · open-access PDF
Semantische Segmentierung von Remote Sensing-Bilder platziert oft Klassengrenzen ein paar Pixel entfernt, wo sie wirklich liegen, und dieser Positionsfehler konzentriert sich in der dünnen Band gemessen von Boundary IoU (BIoU). Wir behandeln Grenzverfeinerung als die wiederholte Anwendung einer kleinen gelernten lokalen Regel: die Boundary Refinement Neural Cellular Automaton (BRNCA), die von der Klasse Wahrscheinlichkeiten eines eingefrorenen, vorbereiteten Segmenters beginnt und korrigiert sie Zelle für Zelle.
Machine translation (Meta M2M100); the English original follows.
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. We generalise the binary rNCA to the multiclass case by keeping the visible channels on the probability simplex through an Aitchison
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