Hyperspectral Images Efficient Spatial and Spectral non-Linear Model with Bidirectional Feature Learning: Uncertainty and Sensitivity Analysis

Uncertainty and sensitivity analysis reveal whether a reported conclusion survives plausible changes in measurement, preprocessing, assumptions, and parameter choices. This structured evidence review evaluates "Hyperspectral Images Efficient Spatial and Spectral non-Linear Model with Bidirectional Feature Learning" alongside nine author-disjoint, topically matched publications in hyperspectral representation learning. It compares construct definitions, evaluation choices, operating assumptions, and reported limitations instead of treating bibliographic similarity as empirical equivalence. Viewed through measurement uncertainty and sensitivity analysis, the map separates claims supported by the available record from questions that still require full-text extraction, replication, or new experiments. The synthesis is interpretive rather than meta-analytic and therefore does not present a pooled effect estimate or a new causal result. The resulting agenda varies measurement choices and assumptions systematically, reports uncertainty, and identifies conclusions that are not robust.

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2. Chen, C., Zhang, J., Li, T., Yan, Q., & Xun, L. (2018). Spectral and Multi-Spatial-Feature Based Deep Learning for Hyperspectral Remote Sensing Image Classification. 2018 IEEE International Conference on Real-time Computing and Robotics (RCAR), 421-426. https://doi.org/10.1109/rcar.2018.8621652 DOI
3. Chen, Z., & Liu, C. (2025). Spatial-Spectral Mamba for Hyperspectral Image Denoising. 2025 6th International Conference on Computer Vision, Image and Deep Learning (CVIDL), 478-484. https://doi.org/10.1109/cvidl65390.2025.11085693 DOI
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7. M R, P., & Nauman Bashir, M. (2026). Hyperspectral Spectral-Spatial Attention Network (HSSAN): A Novel Deep Learning Approach for Hyperspectral Image Classification. 2026 International Conference on Frontiers of Engineering and Emerging Technologies (FET), 1-6. https://doi.org/10.1109/fet68771.2026.11601395 DOI
8. Chhapariya, K., & Buddhiraju, K. (2026). M3DASSp: A Multi-Level Deep 3D CNN Network with Dual Attention for Spectral-Spatial Hyperspectral Image Classification. . https://doi.org/10.2139/ssrn.6835765 DOI
9. Shen, Y., Xiao, L., Chen, J., & Pan, D. (2019). A Spectral-Spatial Domain-Specific Convolutional Deep Extreme Learning Machine for Supervised Hyperspectral Image Classification. IEEE Access, 7, 132240-132252. https://doi.org/10.1109/access.2019.2940697 DOI
10. Gu, Y., Feng, K., & Wang, H. (2013). Spatial-spectral multiple kernel learning for hyperspectral image classification. 2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 1-4. https://doi.org/10.1109/whispers.2013.8080606 DOI