A Memory-Guided Network and a Novel Dataset for Cropland Semantic Change Detection

作者：Mengxi Liu, Simin Lin, Yutong Zhong, Qian Shi, Jiaqi Li · 发表于：IEEE Transactions on Geoscience and Remote Sensing · 年份：2024 · DOI：10.1109/tgrs.2024.3421654 · 被引用次数：10 · 研究领域：Advanced Computational Techniques and Applications、Technology and Security Systems

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) data...