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Multi-stage convolutional autoencoder network for hyperspectral unmixing
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2022-09-11 , DOI: 10.1016/j.jag.2022.102981
Yang Yu, Yong Ma, Xiaoguang Mei, Fan Fan, Jun Huang, Hao Li

Hyperspectral unmixing (HU) is a fundamental and critical task in various hyperspectral image (HSI) applications. Over the past few years, the linear mixing model (LMM) has received widely attention for its high efficiency, definite physical meaning, and being amenable to mathematical treatment. Among the various linear unmixing methods, the autoencoder unmixing network has achieved superior performance and presented more significant potential because of the powerful data fitting ability and deep feature acquisition. However, the autoencoder unmixing network, focusing on the pixel-level associations, ignores the overall distribution and long-range dependencies of materials. Inspired by the receptive field mechanism and the effectiveness of multi-stage framework, we propose a multi-stage convolutional autoencoder network for hyperspectral linear unmixing, called MSNet. MSNet is capable of learning broad contextual information without losing the detailed features by the progressively multi-stage unmixing network in the unmixing process. Compared with the conventional single-stage unmixing methods, the multi-stage framework is more robust in solving the ill-posed unmixing problem. The proposed MSNet performs more effectively and competitively than state-of-the-art algorithms by comparison experiments on synthetic and real hyperspectral datasets. The source code is available at https://github.com/yuyang95/JAG-MSNet.



中文翻译:

用于高光谱分解的多级卷积自动编码器网络

高光谱解混 (HU) 是各种高光谱图像 (HSI) 应用中的一项基本且关键的任务。近年来,线性混合模型(LMM)以其高效、明确的物理意义和易于数学处理而受到广泛关注。在各种线性解混方法中,自编码解混网络因其强大的数据拟合能力和深度特征获取而取得了优越的性能并呈现出更显着的潜力。然而,自动编码器解混合网络,专注于像素级关联,忽略了材料的整体分布和长期依赖关系。受感受野机制和多阶段框架有效性的启发,我们提出了一种用于高光谱线性解混的多级卷积自动编码器网络,称为 MSNet。MSNet 能够在不混合过程中通过逐步多阶段的分解网络学习广泛的上下文信息,而不会丢失详细的特征。与传统的单阶段解混合方法相比,多阶段框架在解决病态解混合问题方面更加鲁棒。通过在合成和真实高光谱数据集上进行比较实验,所提出的 MSNet 比最先进的算法更有效、更具竞争力。源代码可在 https://github.com/yuyang95/JAG-MSNet 获得。与传统的单阶段解混合方法相比,多阶段框架在解决病态解混合问题方面更加鲁棒。通过在合成和真实高光谱数据集上进行比较实验,所提出的 MSNet 比最先进的算法更有效、更具竞争力。源代码可在 https://github.com/yuyang95/JAG-MSNet 获得。与传统的单阶段解混合方法相比,多阶段框架在解决病态解混合问题方面更加鲁棒。通过在合成和真实高光谱数据集上进行比较实验,所提出的 MSNet 比最先进的算法更有效、更具竞争力。源代码可在 https://github.com/yuyang95/JAG-MSNet 获得。

更新日期:2022-09-12
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