Abstract
Cropland resources are experiencing substantial pressure due to the combined effects of global climatic shifts and population growth. Meanwhile, the extensive trend of “cropland conversion” has intensified the loss of cropland, threatening food stability. To tackle this issue, remote sensing has emerged as a crucial approach for observing changes in cropland. However, recognizing changes in cropland using high-resolution remote sensing imagery is still a difficult task because of challenges such as large intra-class variation and small inter-class variation of cropland in complex contexts, insufficient extraction of detail at the edges of cropland, and insufficient optimization stability under imbalanced sample conditions. In response to these challenges, we propose an innovative cropland change detection network, IMEA-Net. Firstly, an intelligent weighted wavelet signal extractor (IWWSE) is introduced to extract low-frequency global background and high-frequency local detail features of cropland from remote sensing images using wavelet transform. Secondly, a novel edge-sensitive mamba (ESMamba) is proposed to enhance the detection of fuzzy boundaries of cropland through spatial modeling and structural adjustment. Finally, an adaptive fusion optimization algorithm (AFOA) is introduced to enhance training stability by dynamically adjusting the learning rate. The experimental evaluation confirms that IMEA-Net outperforms seven state-of-the-art (SOTA) approaches on six benchmark datasets: CLCD, LuojiaSET-CLCD, Hi-CNA, JL1, Fuzhou and PX-CLCD. Furthermore, in practical applications, 50 high-resolution image pairs are collected by the Google Earth API. The results demonstrate that IMEA-Net effectively captures cropland changes and shows promising generalization performance.