In the later training stages, further improvement of the models ability to determine changes relies on how well the change detection (CD) model learns hard cases; however, there are two additional challenges to learning hard case samples: (1) change labels are limited and tend to pointer only to foreground targets, yet hard case samples are prevalent in the background, which leads to optimizing the loss function focusing on the foreground targets and ignoring the background hard cases, which we call imbalance. (2) Complex situations, such as light shadows, target occlusion, and seasonal changes, induce hard case samples, and in the absence of both supervisory and scene information, it is difficult for the model to learn hard case samples directly to accurately obtain the feature representations of the change information, which we call missingness. We propose a Siamese foreground association-driven hard case sample optimization network (HSONet). To deal with this imbalance, we propose an equilibrium optimization loss function to regulate the optimization focus of the foreground and background, determine the hard case samples through the distribution of the loss values, and introduce dynamic weights in the loss term to gradually shift the optimization focus of the loss from the foreground to the background hard cases as the training progresses. To address this missingness, we understand hard case samples with the help of the scene context, propose the scene-foreground association module, use potential remote sensing spatial scene information to model the association between the target of interest in the foreground and the related context to obtain scene embedding, and apply this information to the feature reinforcement of hard cases. Experiments on four public datasets show that HSONet outperforms current state-of-the-art CD methods, particularly in detecting hard case samples.

翻译：在训练的后期阶段，模型判断变化能力的进一步提升取决于变化检测（CD）模型对困难样本的学习效果；然而，学习困难样本面临两个额外挑战：（1）变化标签有限且通常仅指向前景目标，但困难样本普遍存在于背景中，这导致损失函数优化聚焦于前景目标而忽略背景困难样本，我们称之为不平衡性。（2）光照阴影、目标遮挡及季节变化等复杂情境诱发困难样本，在缺乏监督信息和场景信息的情况下，模型难以直接学习困难样本以准确获取变化信息的特征表征，我们称之为缺失性。为此，我们提出一种基于孪生前景关联驱动的困难样本优化网络（HSONet）。针对不平衡性问题，我们提出均衡优化损失函数来调节前景与背景的优化重心，通过损失值分布确定困难样本，并在损失项中引入动态权重，使训练过程中损失的优化焦点从前景逐步转移至背景困难样本。针对缺失性问题，我们借助场景上下文理解困难样本，提出场景-前景关联模块，利用潜在遥感空间场景信息建立前景关注目标与相关上下文的关联以获取场景嵌入，并将此信息应用于困难样本的特征增强。在四个公开数据集上的实验表明，HSONet在检测困难样本方面优于当前最先进的CD方法。