The point spread function (PSF) effect is ubiquitous in remote sensing images and imposes a fundamental uncertainty on subpixel mapping (SPM). The crucial PSF effect has been neglected in existing SPM methods. This paper proposes a general model to reduce the PSF effect in SPM. The model is applicable to any SPM methods treating spectral unmixing as pre-processing. To demonstrate the advantages of the new technique it was necessary to develop a new approach for accuracy assessment of SPM. To-date, accuracy assessment for SPM has been limited to subpixel classification accuracy, ignoring the performance of reproducing spatial structure in downscaling. In this paper, a new accuracy index is proposed which considers SPM performances in classification and restoration of spatial structure simultaneously. Experimental results show that by considering the PSF effect, more accurate SPM results were produced and small-sized patches and elongated features were restored more satisfactorily. Moreover, using the novel accuracy index, the quantitative evaluation was found to be more consistent with visual evaluation. This paper, thus, addresses directly two of the longest standing challenges in SPM (i.e., the limitations of the PSF effect and accuracy assessment undertaken only on a subpixel-by-subpixel basis). © 2020 Elsevier Inc.

中文翻译：

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降低亚像素映射中点扩散函数效应的通用解决方案

点扩散函数 (PSF) 效应在遥感图像中无处不在，并对子像素映射 (SPM) 施加了基本的不确定性。现有的 SPM 方法忽略了关键的 PSF 效应。本文提出了一个通用模型来减少 SPM 中的 PSF 效应。该模型适用于任何将光谱解混作为预处理的 SPM 方法。为了展示新技术的优势，有必要开发一种新的 SPM 精度评估方法。迄今为止，SPM 的精度评估仅限于亚像素分类精度，而忽略了在降尺度中再现空间结构的性能。本文提出了一种新的精度指标，它同时考虑了SPM在空间结构分类和恢复中的性能。实验结果表明，通过考虑PSF效应，产生了更准确的SPM结果，并且更令人满意地恢复了小块和细长特征。此外，使用新的准确度指标，发现定量评估与视觉评估更一致。因此，本文直接解决了 SPM 中两个最长期存在的挑战（即 PSF 效应的局限性和仅在逐个子像素基础上进行的精度评估）。© 2020 爱思唯尔公司 直接解决了 SPM 中两个最长期存在的挑战（即 PSF 效应的局限性和仅在逐个子像素基础上进行的准确性评估）。© 2020 爱思唯尔公司 直接解决了 SPM 中两个最长期存在的挑战（即 PSF 效应的局限性和仅在逐个子像素基础上进行的准确性评估）。© 2020 爱思唯尔公司