A Physically Constrained and Library-Guided Convolutional Autoencoder for Mineral Hyperspectral Unmixing.
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| Title: | A Physically Constrained and Library-Guided Convolutional Autoencoder for Mineral Hyperspectral Unmixing. |
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| Authors: | Hao, Yuxi1 (AUTHOR), Qin, Kai1 (AUTHOR) qinkai@briug.cn, Zhao, Yingjun1 (AUTHOR), Yang, Guofang1 (AUTHOR), Cui, Xin1 (AUTHOR), Zhu, Ling1 (AUTHOR), Yang, Jun1 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 11, p1723. 19p. |
| Subjects: | Hyperspectral imaging systems, Autoencoders |
| Abstract: | Highlights: What are the main findings? A physically constrained and library-guided convolutional autoencoder is proposed for mineral hyperspectral remote sensing unmixing. Compared with existing methods, the proposed method improves endmember spectral fidelity and abundance estimation on simulated data, and yields more geologically consistent endmembers and spatially coherent abundance maps on airborne hyperspectral data. What are the implications of the main findings? Incorporating physically motivated nonlinear scattering constraints enhances the physical consistency and interpretability of deep learning–based hyperspectral remote sensing unmixing. Integrating spectral library priors stabilizes endmember learning and provides a more robust framework for mineral mapping in complex and nonlinearly mixed geological environments. Hyperspectral unmixing is an important technique for mineral mapping because natural geological scenes commonly contain mixed pixels composed of multiple spectrally overlapping materials. In mineral environments, these mixtures are often intimate rather than purely areal, and nonlinear scattering effects may weaken the validity of linear mixing assumptions. Although autoencoder-based hyperspectral unmixing methods can jointly estimate endmembers and abundances in an unsupervised manner, they often suffer from insufficient physical constraints, unstable endmember learning, and limited geological interpretability. To address these issues, this study proposes a physically constrained and library-guided convolutional autoencoder for mineral hyperspectral unmixing. The method retains an interpretable linear reconstruction backbone while introducing a Hapke-consistency regularization term to incorporate physically motivated nonlinear scattering behavior during endmember optimization. In addition, a library-aware endmember anchor module is designed to improve initialization quality, reduce endmember drift, and guide optimization toward spectrally meaningful solutions. The proposed method was evaluated on both simulated hyperspectral datasets and real airborne SASI data. On the simulated datasets, the method achieved improved endmember spectral fidelity and lower abundance estimation error than several representative autoencoder-based baselines, with the advantage being more evident under nonlinear mixing conditions. Ablation experiments further showed that the Hapke-consistency term mainly improved physical plausibility, whereas the anchor module enhanced optimization stability and spectral consistency. On the real airborne dataset, the proposed method produced endmember spectra that were more consistent with field and laboratory mineral references and generated spatially more coherent abundance maps. These results indicate that incorporating physically motivated constraints and mineral-library priors into deep autoencoder frameworks can improve the robustness and interpretability of mineral hyperspectral unmixing. The proposed framework provides a practical direction for hyperspectral mineral mapping in mixed and spectrally complex geological environments. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A physically constrained and library-guided convolutional autoencoder is proposed for mineral hyperspectral remote sensing unmixing. Compared with existing methods, the proposed method improves endmember spectral fidelity and abundance estimation on simulated data, and yields more geologically consistent endmembers and spatially coherent abundance maps on airborne hyperspectral data. What are the implications of the main findings? Incorporating physically motivated nonlinear scattering constraints enhances the physical consistency and interpretability of deep learning–based hyperspectral remote sensing unmixing. Integrating spectral library priors stabilizes endmember learning and provides a more robust framework for mineral mapping in complex and nonlinearly mixed geological environments. Hyperspectral unmixing is an important technique for mineral mapping because natural geological scenes commonly contain mixed pixels composed of multiple spectrally overlapping materials. In mineral environments, these mixtures are often intimate rather than purely areal, and nonlinear scattering effects may weaken the validity of linear mixing assumptions. Although autoencoder-based hyperspectral unmixing methods can jointly estimate endmembers and abundances in an unsupervised manner, they often suffer from insufficient physical constraints, unstable endmember learning, and limited geological interpretability. To address these issues, this study proposes a physically constrained and library-guided convolutional autoencoder for mineral hyperspectral unmixing. The method retains an interpretable linear reconstruction backbone while introducing a Hapke-consistency regularization term to incorporate physically motivated nonlinear scattering behavior during endmember optimization. In addition, a library-aware endmember anchor module is designed to improve initialization quality, reduce endmember drift, and guide optimization toward spectrally meaningful solutions. The proposed method was evaluated on both simulated hyperspectral datasets and real airborne SASI data. On the simulated datasets, the method achieved improved endmember spectral fidelity and lower abundance estimation error than several representative autoencoder-based baselines, with the advantage being more evident under nonlinear mixing conditions. Ablation experiments further showed that the Hapke-consistency term mainly improved physical plausibility, whereas the anchor module enhanced optimization stability and spectral consistency. On the real airborne dataset, the proposed method produced endmember spectra that were more consistent with field and laboratory mineral references and generated spatially more coherent abundance maps. These results indicate that incorporating physically motivated constraints and mineral-library priors into deep autoencoder frameworks can improve the robustness and interpretability of mineral hyperspectral unmixing. The proposed framework provides a practical direction for hyperspectral mineral mapping in mixed and spectrally complex geological environments. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18111723 |
