Neural Network for Blind Unmixing: a novel MatrixConv Unmixing (MCU) Approach

• URL: http://arxiv.org/abs/2503.08745v1
• Date: Tue, 11 Mar 2025 09:41:57 GMT
• Title: Neural Network for Blind Unmixing: a novel MatrixConv Unmixing (MCU) Approach
• Authors: Chao Zhou, Wei Pu, Miguel Rodrigues,
• Abstract summary: Hyperspectral image (HSI) unmixing is a challenging research problem.<n>We propose a MatrixConv Unmixing (MCU) approach for endmember and abundance estimation.<n>The proposed methods are tested for effectiveness on both synthetic and real datasets.
• Score: 13.857076242271669
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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