Pascoassis Souza Santos Meira, Gilson Antonio Giraldi, Rafael Duarte Coelho dos Santos · 2026 · arXiv
DOI: 10.48550/arXiv.2610.30007 · open-access PDF
원격 센서 이미징의 세맨틱 분할은 종종 클래스 경계를 그들이 실제로있는 곳에서 몇 픽셀 떨어져 놓고,이 위치 오류는 Border IoU (BIoU)에 의해 측정 된 얇은 밴드에 집중됩니다. 우리는 경계 정화는 작은 배운 지역 규칙의 반복적 인 응용으로 취급합니다 : 그 경계 정화 신경 세포 자동 (BRNCA), 냉동, 사전 훈련 세그먼터의 클래스 확률에서 시작하여 세포별로 수정합니다.
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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