Abstract Because of the low reflection value of the shadow regions in hyperspectral image (HSI), these regions were deleted directly or ignored in target detection or classification. There are some studies on improving the reflectivity of shadow regions, but it is still difficult to determine the actual substances contained in shadow regions. In this paper, an improved target detection method in shadow regions in HSI is proposed by combining the convolution neural network (CNN) and the adaptive cosine consistency estimation (ACE) of the spectral derivative image. Firstly, the derivative image could be obtained by deriving the original HSI in the spectral dimension. The following steps would be performed simultaneously on the original HSI and the derivative image. Next, the shadow regions in HSI could be determined through two-dimensional (2D) CNN model whose main parameters have been adjusted to optimize the network performance. Finally, the substances contained in the shadow regions would be detected by ACE algorithm. The performance of the proposed method was evaluated and analyzed on the real-world HSIs. The experiments show that the spectral derivation can help to improve the target detection and it is worth taking into account the shadow regions for processing HSI data.

中文翻译：

---

卷积神经网络分类的HSI阴影区域中基于光谱推导的目标检测

摘要 由于高光谱图像（HSI）中阴影区域的反射值较低，在目标检测或分类中直接删除或忽略这些区域。有一些关于提高阴影区域反射率的研究，但仍然很难确定阴影区域中包含的实际物质。本文结合卷积神经网络（CNN）和光谱导数图像的自适应余弦一致性估计（ACE），提出了一种改进的HSI阴影区域目标检测方法。首先，可以通过在光谱维度上推导原始HSI来获得导数图像。以下步骤将在原始 HSI 和衍生图像上同时执行。下一个，HSI 中的阴影区域可以通过二维 (2D) CNN 模型来确定，该模型的主要参数已被调整以优化网络性能。最后，通过ACE算法检测阴影区域中包含的物质。在现实世界的 HSI 上评估和分析了所提出方法的性能。实验表明，光谱推导有助于提高目标检测，值得考虑阴影区域处理 HSI 数据。