Abstract Mixed pixels universally exist in remote sensing images, and they are one of the main obstacles for further improving the accuracy of land cover recognition and classification. Since the concept of sub-pixel mapping (SPM) is proposed, SPM technology has rapidly become an important method to solve the problem of mixed pixels. To further improve SPM accuracy, this paper first proposes a double-calculated spatial attraction model (DSAM) combining the advantages of the spatial attraction model (SAM) and the pixel swap model (PSM). Then, based on the full validation of the proposed DSAM, how multiple factors affect the SPM accuracy is analyzed using the multispectral remote sensing (MRS) images. Finally, by analyzing the maximum variations in the ranges of the overall accuracy and the kappa coefficient under different multiple factors, the order of factors influencing SPM accuracy is determined as follows: reconstruction scale > image spatial resolution > pixel spatial relationships. The results can serve as a reference for other scholars in setting model parameters and selecting the appropriate remote sensing data, thereby helping them achieve more accurate SPM results.

中文翻译：

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重建尺度、空间分辨率和像素空间关系对双计算空间吸引力模型亚像素映射精度的影响

摘要 遥感图像中普遍存在混合像素，是进一步提高土地覆盖识别和分类精度的主要障碍之一。自亚像素映射（SPM）概念提出以来，SPM技术迅速成为解决混合像素问题的重要方法。为了进一步提高SPM精度，本文首先提出了一种结合空间吸引模型（SAM）和像素交换模型（PSM）优点的双计算空间吸引模型（DSAM）。然后，基于对所提出的 DSAM 的全面验证，使用多光谱遥感 (MRS) 图像分析了多种因素如何影响 SPM 精度。最后，通过分析不同多因素下整体精度和kappa系数范围的最大变化，影响SPM精度的因素顺序确定为：重建尺度>图像空间分辨率>像素空间关系。该结果可为其他学者设置模型参数和选择合适的遥感数据提供参考，从而帮助他们获得更准确的SPM结果。