Abstract
High-resolution remote sensing image semantic segmentation (HRSS) is a fundamental yet critical task in the field of Earth observation. However, it has long faced the challenges of high inter-class similarity and large intra-class variability. Existing approaches often struggle to effectively inject abstract yet strongly discriminative semantic knowledge into pixel-level feature learning, leading to blurred boundaries and class confusion in complex scenes. To address these challenges, we propose Bidirectional Co-Refinement Framework for HRSS (BiCoR-Seg). Specifically, we design a Heatmap-driven Bidirectional Information Synergy Module (HBIS), which establishes a bidirectional information flow between feature maps and class embeddings by generating class-level heatmaps. Based on HBIS, we further introduce a hierarchical supervision strategy, where the interpretable heatmaps generated by each HBIS module are directly utilized as low-resolution segmentation predictions for supervision, thereby enhancing the discriminative capacity of shallow features. In addition, to further improve the discriminability of the embedding representations, we propose a cross-layer class embedding Fisher Discriminative Loss to enforce intra-class compactness and enlarge inter-class separability. Extensive experiments on the LoveDA, Vaihingen, and Potsdam datasets demonstrate that BiCoR-Seg achieves outstanding segmentation performance while offering stronger interpretability. The released code is available at this https URL.
Abstract (translated)
高分辨率遥感图像语义分割（HRSS）是地球观测领域的一项基本而关键的任务。然而，长期以来它一直面临着类间相似度高和类内变异性大的挑战。现有方法往往难以有效地将抽象但具有强区分性的语义知识注入到像素级特征学习中，导致复杂场景中的边界模糊和类别混淆问题。 为了应对这些挑战，我们提出了一种双向协同精炼框架（BiCoR-Seg）。具体而言，我们设计了热图驱动的双向信息协同模块（HBIS），该模块通过生成类级别热图在特征图与类别嵌入之间建立了双向的信息流。基于HBIS，我们进一步引入了一个分层监督策略，在此策略中，每个HBIS模块产生的可解释热图直接作为低分辨率分割预测用于监督，从而增强了浅层特征的区分能力。 此外，为了进一步提高嵌入表示的区分性，我们提出了一种跨层类别嵌入费舍尔判别损失（Cross-layer Class Embedding Fisher Discriminative Loss），以强制类内紧致性和扩大类间可分性。在LoveDA、Vaihingen和Potsdam数据集上的广泛实验表明，BiCoR-Seg实现了卓越的分割性能，并提供了更强的解释能力。 相关代码已公开发布在此链接中（https://...）供参考使用。
URL
https://arxiv.org/abs/2512.20255
