Shadow removal is a challenging problem in hyperspectral remote sensing images due to its spatial-variant properties and diverse patterns. In this work, a shadow removal framework with multiexposure fusion is proposed for hyperspectral remote sensing images, which consists of three major steps. First, a color space conversion method is exploited to detect the shadow regions. Second, the principle of the intrinsic decomposition model is utilized to generate a set of differently exposed hyperspectral images (HSIs), i.e., multiexposure images. Third, the generated multiexposure images and the original HSIs are fused together with a two-stage image fusion method so as to remove the shadows in hyperspectral remote sensing images effectively. Experiments performed on three real hyperspectral datasets confirm that the performance of the proposed method outperforms other state-of-the-art shadow removal approaches.

中文翻译：

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利用多重曝光融合去除高光谱遥感图像的阴影


由于其空间变化特性和多样化模式，阴影去除是高光谱遥感图像中的一个具有挑战性的问题。在这项工作中，提出了一种针对高光谱遥感图像的多重曝光融合阴影去除框架，该框架由三个主要步骤组成。首先，利用色彩空间转换方法来检测阴影区域。其次，利用本征分解模型的原理生成一组不同曝光的高光谱图像（HSI），即多重曝光图像。第三，利用两阶段图像融合方法将生成的多重曝光图像与原始HSI融合在一起，从而有效去除高光谱遥感图像中的阴影。在三个真实高光谱数据集上进行的实验证实，所提出的方法的性能优于其他最先进的阴影去除方法。