Keywords
bidirectional feature fusion
band corruption
paired simulation
reproducibility
Abstract
We evaluated bidirectional feature fusion for hyperspectral classification under 27% band corruption. A deterministic paired simulation generated 48 cases and preserved a low-signal stratum. Mean spectral-spatial accuracy changed from 0.524 to 0.576; the paired difference was +0.052 (95% interval +0.050 to +0.055). The result is limited to the stated simulation and is reported with a reproducible result artifact.
References
Yang, J.-X., Wang, J., Long, Z., Sui, C., & Zhou, J. (2024). Hyperspectral Images Efficient Spatial and Spectral non-Linear Model with Bidirectional Feature Learning. arXiv. https://doi.org/10.48550/arXiv.2412.00283
Liu, F., Cheng, S., Yang, Z., Du, A., Huang, H., & Han, R. (2026). Manifold-Aware Spectral Covariance and Directional Anchor Network for Hyperspectral Image Classification. https://doi.org/10.2139/ssrn.6583528
Li, J., Zhou, S., Liu, Y., Hu, G., & Liu, X. (2026). Hyperspectral Image Classification Based on a Spatial-Spectral Dual-Branch Mamba Architecture. Remote Sensing, 18(15), 2526. https://doi.org/10.3390/rs18152526
Munishamaiaha, K., Rajagopal, G., Venkatesan, D. K., Arif, M., Vicoveanu, D., Chiuchisan, I., Izdrui, D., & Geman, O. (2022). Robust Spatial-Spectral Squeeze-Excitation AdaBound Dense Network (SE-AB-Densenet) for Hyperspectral Image Classification. Sensors, 22(9), 3229. https://doi.org/10.3390/s22093229
Feng, F., Wang, S., Wang, C., & Zhang, J. (2019). Learning Deep Hierarchical Spatial-Spectral Features for Hyperspectral Image Classification Based on Residual 3D-2D CNN. Sensors, 19(23), 5276. https://doi.org/10.3390/s19235276
Chen, C., Li, W., Su, H., & Liu, K. (2014). Spectral-Spatial Classification of Hyperspectral Image Based on Kernel Extreme Learning Machine. Remote Sensing, 6(6), 5795-5814. https://doi.org/10.3390/rs6065795
Lu, Z., & He, J. (2015). Spectral-spatial hyperspectral image classification with adaptive mean filter and jump regression detection. Electronics Letters, 51(21), 1658-1660. https://doi.org/10.1049/el.2015.2259
Zhao, G., Wang, X., Kong, Y., & Cheng, Y. (2021). Spectral-Spatial Joint Classification of Hyperspectral Image Based on Broad Learning System. Remote Sensing, 13(4), 583. https://doi.org/10.3390/rs13040583
Rao, Z., & Gai, M. (2025). Adaptive Spectral-Spatial Fusion Mamba A Novel Framework for Enhanced Hyperspectral Image Classification. https://doi.org/10.20944/preprints202511.1470.v1
Chan, R. H., & Li, R. (2022). A 3-Stage Spectral-Spatial Method for Hyperspectral Image Classification. Remote Sensing, 14(16), 3998. https://doi.org/10.3390/rs14163998