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 https://github.com/ShiJinghao566/BiCoR-Seg.

翻译：高分辨率遥感图像语义分割是地球观测领域一项基础而关键的任务。然而，该任务长期面临类间相似性高、类内差异性大的挑战。现有方法往往难以将抽象且判别性强的语义知识有效注入像素级特征学习，导致复杂场景下边界模糊和类别混淆。为应对这些挑战，我们提出了面向高分辨率遥感图像分割的双向协同优化框架（BiCoR-Seg）。具体而言，我们设计了一个热图驱动的双向信息协同模块，该模块通过生成类别级热图，在特征图与类别嵌入之间建立双向信息流。基于此模块，我们进一步引入分层监督策略，将每个模块生成的可解释热图直接用作低分辨率分割预测进行监督，从而增强浅层特征的判别能力。此外，为提升嵌入表示的判别性，我们提出了一种跨层类别嵌入费舍尔判别损失，以增强类内紧凑性并扩大类间分离度。在LoveDA、Vaihingen和Potsdam数据集上的大量实验表明，BiCoR-Seg在实现优异分割性能的同时，提供了更强的可解释性。开源代码发布于 https://github.com/ShiJinghao566/BiCoR-Seg。