We propose a result-level category-specific fusion architecture called ClassWise-CRF. This architecture employs a two-stage process: first, it selects expert networks that perform well in specific categories from a pool of candidate networks using a greedy algorithm; second, it integrates the segmentation predictions of these selected networks by adaptively weighting their contributions based on their segmentation performance in each category. Inspired by Conditional Random Field (CRF), the ClassWise-CRF architecture treats the segmentation predictions from multiple networks as confidence vector fields. It leverages segmentation metrics (such as Intersection over Union) from the validation set as priors and employs an exponential weighting strategy to fuse the category-specific confidence scores predicted by each network. This fusion method dynamically adjusts the weights of each network for different categories, achieving category-specific optimization. Building on this, the architecture further optimizes the fused results using unary and pairwise potentials in CRF to ensure spatial consistency and boundary accuracy. To validate the effectiveness of ClassWise-CRF, we conducted experiments on two remote sensing datasets, LoveDA and Vaihingen, using eight classic and advanced semantic segmentation networks. The results show that the ClassWise-CRF architecture significantly improves segmentation performance: on the LoveDA dataset, the mean Intersection over Union (mIoU) metric increased by 1.00% on the validation set and by 0.68% on the test set; on the Vaihingen dataset, the mIoU improved by 0.87% on the validation set and by 0.91% on the test set. These results fully demonstrate the effectiveness and generality of the ClassWise-CRF architecture in semantic segmentation of remote sensing images. The full code is available at https://github.com/zhuqinfeng1999/ClassWise-CRF.

翻译：我们提出了一种称为ClassWise-CRF的结果级类别特异性融合架构。该架构采用两阶段流程：首先，使用贪心算法从候选网络池中筛选出在特定类别上表现优异的专家网络；其次，根据这些选定网络在各类别上的分割性能自适应地加权其贡献，从而整合它们的分割预测结果。受条件随机场（CRF）启发，ClassWise-CRF架构将来自多个网络的分割预测视为置信度向量场。它利用验证集上的分割度量（如交并比）作为先验，采用指数加权策略融合各网络预测的类别特异性置信度分数。这种融合方法能够针对不同类别动态调整各网络的权重，实现类别特异性优化。在此基础上，该架构进一步利用CRF中的一元势能和成对势能对融合结果进行优化，以确保空间一致性和边界准确性。为验证ClassWise-CRF的有效性，我们在LoveDA和Vaihingen两个遥感数据集上，使用八个经典及先进的语义分割网络进行了实验。结果表明，ClassWise-CRF架构显著提升了分割性能：在LoveDA数据集上，平均交并比（mIoU）指标在验证集上提升了1.00%，在测试集上提升了0.68%；在Vaihingen数据集上，mIoU在验证集上提升了0.87%，在测试集上提升了0.91%。这些结果充分证明了ClassWise-CRF架构在遥感图像语义分割中的有效性和普适性。完整代码发布于 https://github.com/zhuqinfeng1999/ClassWise-CRF。