A spatiotemporal difference-enhancement network with semantic flow alignment for fine-grained cropland change detection

Accurate cropland change detection from high-resolution remote sensing images is essential for agricultural resource monitoring, land management, and food security. Existing methods struggle with two primary challenges: (1) severe pseudo-change noise and false alarms, caused by sole reliance on simple spatial or temporal difference enhancement, and (2) inadequate fine-grained cropland change detection, resulting from boundary detail degradation during fixed interpolation decoding. To address these issues, we propose a S patio T emporal D ifference- E nhancement N etwork (STDENet) with semantic flow alignment for effective cropland change detection from high-resolution remote sensing images. Built on a pyramid vision transformer V2 (PVT-V2) encoder, STDENet integrates three key innovations: a spatiotemporal dual-branch difference enhancement (STDDE) module that jointly captures temporal variations and spatial discrepancies to suppress irrelevant responses from unchanged or spectrally similar regions; an adaptive boundary-aware (ABA) module that dynamically extracts four-directional texture cues to enhance boundary representations; and a semantic flow-aligned decoding (SFAD) module that predicts semantic flow fields to warp high-level difference features and align them with low-level spatial details for fine-grained structure recovery. We evaluate the STDENet on one self-constructed dataset, DYCD, and two public benchmark datasets, CLCD and Hi-CNA. Comparative experiments confirm that STDENet consistently outperforms existing state-of-the-art methods on all three datasets, improving IoU by 3.41 %, 4.90 %, and 1.98 % over the second-best methods on DYCD, CLCD, and Hi-CNA, respectively. By enhancing global structural information through the spatial branch and local change responses through the temporal branch, the spatiotemporal interaction design improves the feature separability of cropland changes. Semantic flow alignment helps restore fine-grained details by producing a more stable decoding process for local contours in cropland change patches. STDENet also demonstrates stronger resistance to interference and better adaptability to phenological variations among crop growth, pre-sowing, and post-harvest periods. Large-scale applications further indicate its generalization ability across heterogeneous cropland structures.