Effect Analysis of Spectral and Spatial Variations on Attention-Based Cropland Extraction Networks

Accurate extraction of cropland is essential for optimizing regional land-use structure and ensuring food security. Although attention-based deep learning has advanced cropland extraction, the lack of a quantitative framework to evaluate the trade-off between spectral band count and spatial resolution hinders optimal sensor configuration. To address this gap, we employ two representative attention-based segmentation networks, BsiNet and REAUnet, to conduct controlled spectral–spatial variation experiments, and proposes an equivalent IoU (Iso-IoU) equivalent model to quantify their complementary relationship. By conducting experiments with multiple band combinations and multi-scale spatial resolutions, we quantitatively evaluate the respective contributions of spectral and spatial information to model performance and further analyze their coupling relationship. The results show that: (1) model performance is positively correlated with spectral richness (i.e., band count), where four-band configurations achieve an IoU improvement of approximately 1.5–4% compared with single-band inputs. While the inclusion of the near-infrared (NIR) band consistently yields the highest accuracy within each band count group, the total number of available spectral bands remains the primary driver of segmentation performance; (2) model performance is more sensitive to spatial resolution, and the IoU decreases by about 5–7% on average when the spatial resolution is degraded to one-quarter of the original resolution; (3) a quantifiable complementary relationship exists between spectral band combinations and spatial resolution, which can be described by the proposed Iso-IoU model; (4) the two attention-based networks examined in this study exhibit stable error tendencies in cropland extraction, with consistent false-positive and false-negative patterns. These findings provide practical guidance for cropland extraction with remote sensing images. Prioritizing NIR information and maintaining sufficient spatial resolution are critical for preserving segmentation accuracy, while the Iso-IoU model enables quantitative optimization of spectral–spatial configurations under sensor constraints.