Changes in cropland are among the most widespread transitions on the Earth surface, significantly impacting food security, ecological conservation, and social stability. Compared to conventional change events, cropland changes involve complex dynamic transformations of semantic representations within the land system, requiring the identification of both the locations and categories of changes. Despite numerous remote sensing change detection methods have been proposed in previous studies, two challenges in cropland semantic change detection (SCD) still deserve further discussion: 1) transition confusions between similar categories and 2) under-labeling and class imbalance related to semantic labels. To address these challenges, we propose a difference enhancement and class-aware rebalancing semi-supervised network (Semi-DECRNet) for cropland SCD. The proposed Semi-DECRNet is implemented in a multi-task three-branch architecture, incorporating a multi-scale semantic aggregation difference enhancement module to couple the semantic and initial differential features at both global and local levels to model the temporal and causal relationships among the binary change detection and semantic segmentation branches. Additionally, a class-aware rebalancing self-training strategy is developed to adaptively calibrate the pseudo-label thresholds and further mine the semantic knowledge in unchanged areas. Experiments and analysis on three benchmark datasets demonstrate the effectiveness and superiority of the proposed Semi-DECRNet method for the cropland SCD task. Code is available at https://github.com/DaiAnjin/Semi-DECRNet.

中文翻译：

---

一种用于农田语义变化检测的差分增强和类感知再平衡半监督网络


农田的变化是地球表面最广泛的转变之一，对粮食安全、生态保护和社会稳定产生了重大影响。与传统的变化事件相比，农田变化涉及土地系统内语义表示的复杂动态转换，需要识别变化的位置和类别。尽管以前的研究中已经提出了许多遥感变化检测方法，但农田语义变化检测 （SCD） 中的两个挑战仍然值得进一步讨论：1） 相似类别之间的过渡混淆和 2） 与语义标签相关的标记不足和类别不平衡。为了应对这些挑战，我们提出了一种用于农田 SCD 的差异增强和类感知再平衡半监督网络 （Semi-DECRNet）。所提出的 Semi-DECRNet 在多任务三分支架构中实现，包含一个多尺度语义聚合差异增强模块，以耦合全局和局部级别的语义和初始差异特征，以模拟二进制变化检测和语义分割分支之间的时间和因果关系。此外，开发了一种类感知再平衡自我训练策略，以自适应地校准伪标签阈值，并进一步挖掘未改变区域中的语义知识。在三个基准数据集上的实验和分析证明了所提出的 Semi-DECRNet 方法对农田 SCD 任务的有效性和优越性。代码可在 https://github.com/DaiAnjin/Semi-DECRNet 获取。