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

Pascoassis Souza Santos Meira, Gilson Antonio Giraldi,  Rafael Duarte Coelho dos Santos · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.30007 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

リモートセンサー画像のセマンティックセグメントはしばしばクラス境界をそれらが実際に存在する場所から数ピクセル離れた場所に置き、このポジションエラーは、Border IoU (BIoU) によって測定された薄い帯域に集中しています。我々は、小規模な学習したローカルルルルルールの繰り返しのアプリケーションとして、境界線の精製を扱います:Border Refinement Neural Cellular Automaton (BRNCA) は、凍結された、事前訓練されたセグメントのクラス確率から始まり、それらを細胞ごとに修正します。我々は、Aitchison (ログスペース) アップデートを通じて確率シンプルに視覚的なチャンネルを保持して、マ

Machine translation (Meta M2M100); the English original follows.

Abstract

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 (log-space) update and by adding free hidden channels. The best configuration improves the mean BIoU over the base segmenter by 2.36 points (from 46.63 to 48.99). 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.

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Topics: Boundary (topology), Computer science, Segmentation, Artificial intelligence, Remote sensing, Pixel

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