Abstract Spectral unmixing is an important task for hyperspectral remote sensing image processing, which infers the pure spectral signatures (endmembers) in hyperspectral image (HSI) and their corresponding fractions (abundances). Recently, deep learning has become a powerful tool for HSI analysis, such as HSI classification and HSI super-resolution. In this paper, we propose a new unmixing algorithm that uses the convolutional neural network (CNN) for hyperspectral data incorporating spectral library, which can be applied for a series of HSIs after training. The proposed deep spectral convolution network extracts features and then executes the estimating process from these extracted spectral characteristics to acquire the fractional abundances on a fixed spectral library. Meanwhile, considering the incorporation of spectral library, a deeper convolutional network has been adopted to achieve better results. Moreover, we construct a new loss function, which includes pixel reconstruction error, abundance sparsity, and abundance cross-entropy to train the aforementioned network in an end-to-end manner. Experiments on both simulated and real HSIs indicate the advantage of the proposed method, which can obviously enhance the abundance estimation accuracy.

中文翻译：

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用于与光谱库分离的高光谱图像的深度光谱卷积网络

摘要 光谱解混是高光谱遥感图像处理的一项重要任务，它推断高光谱图像（HSI）中的纯光谱特征（端元）及其相应的分数（丰度）。最近，深度学习已成为 HSI 分析的强大工具，例如 HSI 分类和 HSI 超分辨率。在本文中，我们提出了一种新的解混算法，该算法使用卷积神经网络 (CNN) 处理包含光谱库的高光谱数据，训练后可应用于一系列 HSI。所提出的深度光谱卷积网络提取特征，然后从这些提取的光谱特征中执行估计过程，以获得固定光谱库上的分数丰度。同时，考虑到光谱库的合并，为了达到更好的效果，采用了更深的卷积网络。此外，我们构建了一个新的损失函数，其中包括像素重建误差、丰度稀疏性和丰度交叉熵，以端到端的方式训练上述网络。在模拟和真实 HSI 上的实验表明了该方法的优势，可以明显提高丰度估计的准确性。