Temperature scaling unmixing framework based on convolutional autoencoder
•Different degrees of sparsity constraints are imposed adaptively in deep learning networks by temperature scaling techniques.•By considering the distribution of ground objects, it has good generalization in the real scene.•The framework is a new spatial level constraint method and can be transferre...
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| Published in: | International journal of applied earth observation and geoinformation Vol. 129; p. 103864 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.05.2024
Elsevier |
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| ISSN: | 1569-8432, 1872-826X |
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| Abstract | •Different degrees of sparsity constraints are imposed adaptively in deep learning networks by temperature scaling techniques.•By considering the distribution of ground objects, it has good generalization in the real scene.•The framework is a new spatial level constraint method and can be transferred to other convolutional autoencoder-based methods.
Hyperspectral unmixing is a key technology in the development of remote sensing applications. However, since both endmembers and abundances are unknown, unmixing is a non-convex problem with a large solution space. To solve this, existing methods usually impose the same strength of sparsity constraint. However, this often does not hold in practice. Because the abundances of purer regions are generally sparse, while the abundances distribution of more mixed regions should be smoother. Temperature scaling is a technique of introducing a temperature parameter T into softmax activation function to adjust the sparsity of the output. Inspired by this, we propose a temperature scaling unmixing (TSU) framework based on convolutional autoencoder (CAE). In this framework, sparse constraints of different intensities are applied to diverse regions by considering spatial similarity of ground objects distribution while preserving the ability of CAE to extract spatial features. What is more, equal-frequency binning is adopted to guide the division of regions by similarity matrix to realize the automatic temperature parameter setting. In addition, a CAE network is designed under the TSU framework in this paper, called TSUCAE. The TSUCAE method exhibits superior accuracy compared to state-of-the-art approaches, as demonstrated through extensive comparative experiments. Furthermore, the TSU framework can be transferred to other CAE-based unmixing methods directly while keeping the network structure of these methods unchanged. Sufficient ablation experiments also prove that the transfer of framework can improve the performance of unmixing. The code is publicly available at https://github.com/UPCGIT/TSUCAE. |
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| AbstractList | •Different degrees of sparsity constraints are imposed adaptively in deep learning networks by temperature scaling techniques.•By considering the distribution of ground objects, it has good generalization in the real scene.•The framework is a new spatial level constraint method and can be transferred to other convolutional autoencoder-based methods.
Hyperspectral unmixing is a key technology in the development of remote sensing applications. However, since both endmembers and abundances are unknown, unmixing is a non-convex problem with a large solution space. To solve this, existing methods usually impose the same strength of sparsity constraint. However, this often does not hold in practice. Because the abundances of purer regions are generally sparse, while the abundances distribution of more mixed regions should be smoother. Temperature scaling is a technique of introducing a temperature parameter T into softmax activation function to adjust the sparsity of the output. Inspired by this, we propose a temperature scaling unmixing (TSU) framework based on convolutional autoencoder (CAE). In this framework, sparse constraints of different intensities are applied to diverse regions by considering spatial similarity of ground objects distribution while preserving the ability of CAE to extract spatial features. What is more, equal-frequency binning is adopted to guide the division of regions by similarity matrix to realize the automatic temperature parameter setting. In addition, a CAE network is designed under the TSU framework in this paper, called TSUCAE. The TSUCAE method exhibits superior accuracy compared to state-of-the-art approaches, as demonstrated through extensive comparative experiments. Furthermore, the TSU framework can be transferred to other CAE-based unmixing methods directly while keeping the network structure of these methods unchanged. Sufficient ablation experiments also prove that the transfer of framework can improve the performance of unmixing. The code is publicly available at https://github.com/UPCGIT/TSUCAE. Hyperspectral unmixing is a key technology in the development of remote sensing applications. However, since both endmembers and abundances are unknown, unmixing is a non-convex problem with a large solution space. To solve this, existing methods usually impose the same strength of sparsity constraint. However, this often does not hold in practice. Because the abundances of purer regions are generally sparse, while the abundances distribution of more mixed regions should be smoother. Temperature scaling is a technique of introducing a temperature parameter T into softmax activation function to adjust the sparsity of the output. Inspired by this, we propose a temperature scaling unmixing (TSU) framework based on convolutional autoencoder (CAE). In this framework, sparse constraints of different intensities are applied to diverse regions by considering spatial similarity of ground objects distribution while preserving the ability of CAE to extract spatial features. What is more, equal-frequency binning is adopted to guide the division of regions by similarity matrix to realize the automatic temperature parameter setting. In addition, a CAE network is designed under the TSU framework in this paper, called TSUCAE. The TSUCAE method exhibits superior accuracy compared to state-of-the-art approaches, as demonstrated through extensive comparative experiments. Furthermore, the TSU framework can be transferred to other CAE-based unmixing methods directly while keeping the network structure of these methods unchanged. Sufficient ablation experiments also prove that the transfer of framework can improve the performance of unmixing. The code is publicly available at https://github.com/UPCGIT/TSUCAE. |
| ArticleNumber | 103864 |
| Author | Sheng, Hui Yang, Zhiru Xu, Jin Xu, Mingming Liu, Shanwei |
| Author_xml | – sequence: 1 givenname: Jin surname: Xu fullname: Xu, Jin – sequence: 2 givenname: Mingming orcidid: 0000-0002-6758-9863 surname: Xu fullname: Xu, Mingming email: xumingming@upc.edu.cn, xumingming900405@126.com – sequence: 3 givenname: Shanwei surname: Liu fullname: Liu, Shanwei – sequence: 4 givenname: Hui surname: Sheng fullname: Sheng, Hui – sequence: 5 givenname: Zhiru surname: Yang fullname: Yang, Zhiru |
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| Keywords | Hyperspectral unmixing Temperature scaling Sparsity constraint Convolutional autoencoder Equal-frequency binning |
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| Title | Temperature scaling unmixing framework based on convolutional autoencoder |
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