Title: Neural Network for Blind Unmixing: a novel MatrixConv Unmixing (MCU) Approach Show Chinese title

Title: 神经网络用于盲解混：一种新的矩阵卷积解混（MCU）方法

Abstract: Hyperspectral image (HSI) unmixing is a challenging research problem that tries to identify the constituent components, known as endmembers, and their corresponding proportions, known as abundances, in the scene by analysing images captured by hyperspectral cameras. Recently, many deep learning based unmixing approaches have been proposed with the surge of machine learning techniques, especially convolutional neural networks (CNN). However, these methods face two notable challenges: 1. They frequently yield results lacking physical significance, such as signatures corresponding to unknown or non-existent materials. 2. CNNs, as general-purpose network structures, are not explicitly tailored for unmixing tasks. In response to these concerns, our work draws inspiration from double deep image prior (DIP) techniques and algorithm unrolling, presenting a novel network structure that effectively addresses both issues. Specifically, we first propose a MatrixConv Unmixing (MCU) approach for endmember and abundance estimation, respectively, which can be solved via certain iterative solvers. We then unroll these solvers to build two sub-networks, endmember estimation DIP (UEDIP) and abundance estimation DIP (UADIP), to generate the estimation of endmember and abundance, respectively. The overall network is constructed by assembling these two sub-networks. In order to generate meaningful unmixing results, we also propose a composite loss function. To further improve the unmixing quality, we also add explicitly a regularizer for endmember and abundance estimation, respectively. The proposed methods are tested for effectiveness on both synthetic and real datasets. Show Chinese abstract

Abstract: 高光谱图像（HSI）分解是一个具有挑战性的研究问题，旨在通过分析由高光谱相机捕获的图像来识别场景中的组成成分（称为端元）及其对应的丰度比例。最近，随着机器学习技术的兴起，特别是卷积神经网络（CNN），许多基于深度学习的分解方法被提出。然而，这些方法面临两个显著挑战：1. 它们经常产生缺乏物理意义的结果，例如对应于未知或不存在材料的特征签名；2. CNN作为一种通用网络结构，并未明确针对分解任务进行优化。为了解决这些问题，我们的工作受到双层深度图像先验（DIP）技术和算法展开的启发，提出了一种新的网络结构以有效解决上述问题。具体而言，我们首先分别提出了用于端元和丰度估计的矩阵卷积分解（MCU）方法，该方法可以通过某些迭代求解器解决。然后我们将这些求解器展开构建两个子网络，即端元估计DIP（UEDIP）和丰度估计DIP（UADIP），分别生成端元和丰度的估计值。整体网络通过组合这两个子网络构建而成。为了生成有意义的分解结果，我们还提出了一种复合损失函数。为进一步提高分解质量，我们还分别为端元和丰度估计显式地添加了正则化项。所提出的算法在合成数据集和真实数据集上均进行了有效性测试。