期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 19, 期 -, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2021.3100992
关键词
Libraries; Hyperspectral imaging; Convolutional neural networks; Signal to noise ratio; Optimization; Collaboration; Visualization; Convolutional neural network; deep learning; deep prior; endmember extraction; hyperspectral image (HSI); unmixing
类别
资金
- Alexander-von-Humboldt-Stiftung
The proposed sparse unmixing technique using a convolutional neural network (SUnCNN) for hyperspectral images introduces a new deep learning-based approach that outperforms existing methods in terms of signal to reconstruction error (SRE) and visual presentation on both real and synthetic datasets.
In this letter, we propose a sparse unmixing technique using a convolutional neural network (SUnCNN) for hyperspectral images. SUnCNN is the first deep learning-based technique proposed for sparse unmixing. It uses a deep convolutional encoder-decoder to generate the abundances relying on a spectral library. We reformulate the sparse unmixing into an optimization over the deep network's parameters. Therefore, the deep network learns in an unsupervised manner to map a fixed input into the sparse optimum abundances. Additionally, SUnCNN holds the sum-to-one constraint using a softmax activation layer. The proposed method is compared with the state-of-the-art using two synthetic datasets and one real hyperspectral dataset. The overall results confirm that the proposed method outperforms the other ones in terms of signal to reconstruction error (SRE). Additionally, SUnCNN shows visual superiority for both real and synthetic datasets compared with the competing techniques. The proposed method was implemented in Python (3.8) using PyTorch as the platform for the deep network and is available online: https://github.com/BehnoodRasti/SUnCNN.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据