Newest papers

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

Chinese

远程传感图像的语义细分通常会将阶级边界置于它们真正存在的地方几像素之外,并且这种位置错误集中在边界IoU(BIoU)测量的薄带中。我们将边界细分作为一个小学到的本地规则的迭代应用:边界细胞神经自动(BRNCA),它从被冻结的、预训练的细分器的阶级概率开始,并将它们细胞以细胞进行纠正。我们将二进制rNCA概括为多类案例,通过一个Aitchison(日志空间)更新并通过添加免费隐藏的渠道来保持在概率简单上可见的渠道。最佳配置提高了基层细分的平均BIooo为2.36点(从46.63到48.99)。

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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