Abstract
Onboard satellite models often require frequent updates, but the weights adapted to earlier data distributions can quickly become outdated. However, updating large-scale model parameters in orbit presents significant challenges due to the limited uplink bandwidth of Low Earth Orbit (LEO) satellite systems, particularly for hyperspectral satellite imagery, where high-dimensional spectral-spatial inputs lead to increased model size and update costs. Existing full fine-tuning methods are thus expensive to retrain and difficult to deploy under strict communication constraints. To address this challenge, we propose NE-LoRA, a parameter-efficient adaptation framework for bandwidth-constrained onboard hyperspectral model updates. NE-LoRA combines a primary low-rank branch with a nonlinear auxiliary branch to capture both global update trends and complex spectral-spatial variations. Additionally, we introduce a differentiated training strategy for multi-matrix adapters, motivated by the asymmetric initialization and gradient dynamics of different adapter matrices. Experiments on four hyperspectral datasets and three representative backbone models demonstrate that NE-LoRA consistently outperforms LoRA-based baselines and remains competitive with, and in several cases superior to, full fine-tuning. Across the evaluated settings, NE-LoRA updates only a small fraction of the total parameters on average while preserving low deployment overhead, offering a favorable accuracy-communication trade-off for onboard hyperspectral adaptation.
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Publication details
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- Open access
- Green open access
Cite this article
APA 7
Zhang, Q., Li, X., Shi, L., Lin, Z., Wen, J., Zhou, A., & Wang, S. (2026). Resource-Aware Parameter-Efficient Model Adaptation for Onboard High-Dimensional Data. https://omanscience.com/en/articles/resource-aware-parameter-efficient-model-adaptation-for-onboard-high-dimensional-data
MLA 9
Zhang, Qiyang, et al. "Resource-Aware Parameter-Efficient Model Adaptation for Onboard High-Dimensional Data." https://omanscience.com/en/articles/resource-aware-parameter-efficient-model-adaptation-for-onboard-high-dimensional-data.
Chicago (author–date)
Zhang, Qiyang, Xinhao Li, Lei Shi, Zheng Lin, Jinfeng Wen, Ao Zhou, and Shangguang Wang. 2026. "Resource-Aware Parameter-Efficient Model Adaptation for Onboard High-Dimensional Data." https://omanscience.com/en/articles/resource-aware-parameter-efficient-model-adaptation-for-onboard-high-dimensional-data.
Harvard
Zhang, Q., Li, X., Shi, L., Lin, Z., Wen, J., Zhou, A. and Wang, S. (2026) 'Resource-Aware Parameter-Efficient Model Adaptation for Onboard High-Dimensional Data', Available at: https://omanscience.com/en/articles/resource-aware-parameter-efficient-model-adaptation-for-onboard-high-dimensional-data.
Vancouver
Zhang Q, Li X, Shi L, Lin Z, Wen J, Zhou A, et al. Resource-Aware Parameter-Efficient Model Adaptation for Onboard High-Dimensional Data. https://omanscience.com/en/articles/resource-aware-parameter-efficient-model-adaptation-for-onboard-high-dimensional-data
IEEE
Q. Zhang, X. Li, L. Shi, Z. Lin, J. Wen, A. Zhou, and S. Wang, "Resource-Aware Parameter-Efficient Model Adaptation for Onboard High-Dimensional Data," https://omanscience.com/en/articles/resource-aware-parameter-efficient-model-adaptation-for-onboard-high-dimensional-data.