The frequent occurrence of nonagriculturalization events has posed significant challenges to global food security and sustainable development. Despite the emerging deep learning (DL) algorithms demonstrating effective capability in capturing changes from remote sensing imagery, they have not consistently maintained favorable performance in cropland semantic change detection (CropSCD) tasks. The primary challenge lies in the natural contradiction between the diverse change classes and the sparse availability of change samples. Furthermore, the scarcity of CropSCD datasets also restricts the capabilities of data-driven models. Therefore, in order to encode diverse semantics from a small amount of change pixels, a memory-guided network (MeGNet) dedicated to CropSCD tasks is proposed. In particular, a class-aware memory module is introduced in the MeGNet to preserve change semantics, which can guide the model to distinguish different change classes. Moreover, a high-resolution CropSCD dataset is also constructed to alleviate the issue of insufficient dataset. The CropSCD dataset comprises 4141 pairs of images, each with a size of $512\times 512$ , and is annotated with corresponding labels for eight cropland change classes. Comparative experiments have substantiated the superiority of MeGNet over current state-of-the-art (SOTA) methods, with the highest mean-F1 and mean intersection over union (mIoU) of 71.44% and 58.01% on the high-resolution semantic change detection (HRSCD) dataset, and those of 55.42% and 42.14% on the CropSCD dataset. These results have validated the feasibility and potential of the proposed MeGNet and CropSCD dataset in CropSCD tasks.

中文翻译：

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用于农田语义变化检测的记忆引导网络和新数据集


非农化事件频发，给全球粮食安全和可持续发展带来重大挑战。尽管新兴的深度学习（DL）算法展示了捕获遥感图像变化的有效能力，但它们在农田语义变化检测（CropSCD）任务中并未始终保持良好的性能。主要挑战在于变化类别的多样性与变化样本的稀缺性之间的天然矛盾。此外，CropSCD 数据集的稀缺也限制了数据驱动模型的能力。因此，为了从少量变化像素中编码不同的语义，提出了一种专用于 CropSCD 任务的记忆引导网络（MeGNet）。特别是，MeGNet 中引入了类感知内存模块来保留变化语义，这可以指导模型区分不同的变化类。此外，还构建了高分辨率的CropSCD数据集以缓解数据集不足的问题。 CropSCD 数据集包含 4141 对图像，每对图像的大小为$512\乘以512$ ，并用八个农田变化类别的相应标签进行注释。对比实验证实了 MeGNet 相对于当前最先进 (SOTA) 方法的优越性，在高分辨率语义变化检测上最高的平均 F1 和平均交集比并集 (mIoU) 分别为 71.44% 和 58.01% (HRSCD) 数据集，以及 CropSCD 数据集上的 55.42% 和 42.14%。这些结果验证了所提出的 MeGNet 和 CropSCD 数据集在 CropSCD 任务中的可行性和潜力。