[
    {
        "id": "osp-24680",
        "type": "article-journal",
        "title": "Resource-Aware Parameter-Efficient Model Adaptation for Onboard High-Dimensional Data",
        "author": [
            {
                "family": "Zhang",
                "given": "Qiyang"
            },
            {
                "family": "Li",
                "given": "Xinhao"
            },
            {
                "family": "Shi",
                "given": "Lei"
            },
            {
                "family": "Lin",
                "given": "Zheng"
            },
            {
                "family": "Wen",
                "given": "Jinfeng"
            },
            {
                "family": "Zhou",
                "given": "Ao"
            },
            {
                "family": "Wang",
                "given": "Shangguang"
            }
        ],
        "URL": "https://omanscience.com/en/articles/resource-aware-parameter-efficient-model-adaptation-for-onboard-high-dimensional-data",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "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models often require frequent updates, but the weights adapted to earlier data distributions can quickly become outdated.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."
    }
]