Efficient Object Detection in Remote Sensing Images: Advances in Deep Network Compression and Acceleration from Algorithmic and Model-Scale Perspectives

WANG Liuqian; ZHANG Jing; ZHAO Yi; XIE Xiaoyang; ZHUO Li

doi:10.12263/DZXB.20251009

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Efficient Object Detection in Remote Sensing Images: Advances in Deep Network Compression and Acceleration from Algorithmic and Model-Scale Perspectives

SURVEYS AND REVIEWS | 更新时间：2026-06-16

- Efficient Object Detection in Remote Sensing Images: Advances in Deep Network Compression and Acceleration from Algorithmic and Model-Scale Perspectives
- ACTA ELECTRONICA SINICA Vol. 54, Issue 3, Pages: 1348-1363(2026)
- 作者机构：
  
  1.北京工业大学信息科学技术学院，北京 100124
  2.郑州大学网络空间安全学院，河南郑州 450002
  3.计算智能与智能系统北京市重点实验室，北京 100124
  4.北京工业大学计算机学院，北京 100124
- 作者简介：
- 基金信息：
  
  Beijing Natural Science Foundation(L247025)
- DOI：10.12263/DZXB.20251009
  CLC： TP751.1;
- Received：11 December 2025，
  
  Accepted：13 January 2026，
  
  Published：25 March 2026
- 稿件说明：
移动端阅览
王柳谦, 张菁, 赵一, 等. 高效遥感图像目标检测：算法与模型规模双视角的深度网络压缩与加速技术进展[J]. 电子学报, 2026, 54(03): 1348-1363.

WANG Liuqian, ZHANG Jing, ZHAO Yi, et al. Efficient Object Detection in Remote Sensing Images: Advances in Deep Network Compression and Acceleration from Algorithmic and Model-Scale Perspectives[J]. Acta Electronica Sinica, 2026, 54(03): 1348-1363.
王柳谦, 张菁, 赵一, 等. 高效遥感图像目标检测：算法与模型规模双视角的深度网络压缩与加速技术进展[J]. 电子学报, 2026, 54(03): 1348-1363. DOI：10.12263/DZXB.20251009

WANG Liuqian, ZHANG Jing, ZHAO Yi, et al. Efficient Object Detection in Remote Sensing Images: Advances in Deep Network Compression and Acceleration from Algorithmic and Model-Scale Perspectives[J]. Acta Electronica Sinica, 2026, 54(03): 1348-1363. DOI：10.12263/DZXB.20251009

摘要

近年来，卷积神经网络（Convolutional Neural Network，CNN）、视觉Transformer以及大规模语言模型（Large Language Model，LLM）等深度学习模型在表示学习与任务泛化方面取得显著进展。然而，随着模型参数规模与计算复杂度迅速增加，其推理延迟与存储开销持续攀升。在遥感领域，高分辨率遥感图像与合成孔径雷达（Synthetic Aperture Radar，SAR）图像具有幅面大、分辨率高、场景复杂、目标多尺度以及小目标占比高等特点。因此，目标检测模型不仅需具备强大的表征能力以应对复杂背景与尺度变化，还必须满足星载、机载及边缘设备等受限平台对实时性与低功耗的严格要求，这使得“精度提升”与“高效部署”之间的矛盾日益凸显。本文首先系统回顾了深度网络压缩与加速技术的发展历程，涵盖从传统手工优化到剪枝、量化与知识蒸馏等经典方法，并进一步探讨了大模型时代下的条件计算与推理优化策略，包括混合专家系统（Mixture of Experts，MoE）、键值（Key Value，KV）缓存优化与混合量化等。其次，通过整合遥感目标检测领域的主流数据集与评价指标体系，本文构建了一个面向“精度-效率-资源”权衡的多维评估基准，用于分析不同压缩方案在遥感场景中的适配性与局限性。与现有综述多沿单一算法主线或仅聚焦轻量网络不同，本文从“算法视角”与“模型规模视角”两个互补维度组织研究脉络：算法视角侧重于在既定模型上通过剪枝、量化、蒸馏与稀疏化来降低计算与存储复杂度；模型规模视角则区分轻量模型与大规模基础模型在结构冗余、敏感模块与内存瓶颈方面的差异，进而分析压缩策略在不同体量模型中的作用机制与优化潜力。再次，基于这一双视角框架，本文对代表性方法的核心思想与实现要点进行对比，总结影响压缩效果的关键因素，并提炼可复用的工程化实践建议，以期为模型选型与部署配置提供参考。最后，结合遥感智能解译的应用需求与绿色低碳发展趋势，本文展望自适应压缩、多模态协同压缩、参数高效微调与软硬件协同优化等方向，为遥感目标检测模型在边缘端与工程系统中的高效落地提供系统化的理论支持与实践指导。

Abstract

In recent years

deep learning models such as convolutional neural networks (CNNs)

vision transformers

and large language models (LLMs) have made remarkable breakthroughs in representation learning and task generalization. However

this progress has been accompanied by rapid increase in parameter scale and computational complexity

leading to rising inference latency and storage overhead. In remote sensing applications

high-resolution remote sensing images and synthetic aperture radar (SAR) images are characterized by large coverage

high resolution

complex scenes

and multi-scale variations with a high proportion of small objects. Object detection models must therefore possess strong representational capability to handle complex backgrounds and scale variations

while also meeting stringent real-time and low-power constraints on resource-limited platforms such as satellite-borne

airborne

and edge devices. As a result

the tension between “improving accuracy” and “efficient deployment” has become increasingly pronounced. This study systematically reviews the development of deep network compression and acceleration techniques

covering traditional manual optimizations as well as classical methods such as pruning

quantization

and knowledge distillation. It further discusses conditional computation and inference optimization in the era of large models

including mixture-of-experts (MoE) architectures

key-value (KV) cache optimization

and hybrid quantization. By integrating mainstream datasets and evaluation metrics in the field of remote sensing object detection

this paper establishes a multi-dimensional evaluation benchmark oriented toward the “accuracy-efficiency-resource” trade-offs

aimed at characterizing the applicability and limitations of different compression approaches in remote sensing scenarios. Unlike existing surveys that often follow a single algorithmic thread or focus solely on lightweight networks

this review organizes the research landscape from two complementary perspectives: the algorithmic perspective and the model-scale perspective. The former emphasizes reducing computational and storage complexity through pruning

quantization

distillation

and sparsification on a given model. The latter distinguishes between lightweight models and large-scale foundation in terms of structural redundancy

sensitive modules

and memory bottlenecks

analyzing how compression strategies operate and where further optimization is possible across models of different sizes. Based on this dual-perspective framework

the paper compares and analyzes the core ideas and implementation details of representative methods

summarizes key factors influencing compression performance

and extracts reusable engineering practices to inform model selection and deployment configurations. Finally

considering the application demands of remote sensing intelligent interpretation and the trend toward green and low-carbon computing

this paper looks ahead to promising directions such as adaptive compression

multimodal collaborative-compression

parameter-efficient fine-tuning

and hardware-software co-optimization. The aim is to provide systematic reference and theoretical support for the efficient deployment of remote sensing object detection models on edge devices and in engineering systems.

关键词

Keywords

references

Lu S Q , Guo J L , Zimmer-Dauphinee J R , et al . Vision foundation models in remote sensing: A survey [J ] . IEEE Geoscience and Remote Sensing Magazine , 2025 , 13 ( 3 ): 190 - 215 . DOI: 10.1109/mgrs.2025.3541952 http://dx.doi.org/10.1109/mgrs.2025.3541952

Ren S Q , He K M , Girshick R , et al . Faster R-CNN: Towards real-time object detection with region proposal networks [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2017 , 39 ( 6 ): 1137 - 1149 . DOI: 10.1109/tpami.2016.2577031 http://dx.doi.org/10.1109/tpami.2016.2577031

Chen Z , Wu J N , Wang W H , et al . Intern VL: Scaling up vision foundation models and aligning for generic visual-linguistic tasks [C ] // 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2024 : 24185 - 24198 . DOI: 10.1109/cvpr52733.2024.02283 http://dx.doi.org/10.1109/cvpr52733.2024.02283

苏航 , 徐从安 , 姚力波 , 等 . 一种轻量化SAR图像舰船目标斜框检测方法 [J ] . 航空学报 , 2022 , 43 ( S1 ): 157 - 164 . DOI: 10.7527/S1000-6893.2022.26922 http://dx.doi.org/10.7527/S1000-6893.2022.26922

Su Hang , Xu Congan , Yao Libo , et al . A lightweight oriented ship detection method in SAR images [J ] . Acta Aeronautica et Astronautica Sinica , 2022 , 43 ( S1 ): 157 - 164 . (in Chinese) . DOI: 10.7527/S1000-6893.2022.26922 http://dx.doi.org/10.7527/S1000-6893.2022.26922

Zubko V , Kaufman Y J , Burg R I , et al . Principal component analysis of remote sensing of aerosols over oceans [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2007 , 45 ( 3 ): 730 - 745 . DOI: 10.1109/tgrs.2006.888138 http://dx.doi.org/10.1109/tgrs.2006.888138

Jia S , Qian Y T . Constrained nonnegative matrix factorization for hyperspectral unmixing [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2009 , 47 ( 1 ): 161 - 173 . DOI: 10.1109/tgrs.2008.2002882 http://dx.doi.org/10.1109/tgrs.2008.2002882

Johansen K , Raharjo T , McCabe M F . Using multi-spectral UAV imagery to extract tree crop structural properties and assess pruning effects [J ] . Remote Sensing , 2018 , 10 ( 6 ): 854 . DOI: 10.3390/rs10060854 http://dx.doi.org/10.3390/rs10060854

Li P , Han L R , Tao X W , et al . Hashing nets for hashing: A quantized deep learning to hash framework for remote sensing image retrieval [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2020 , 58 ( 10 ): 7331 - 7345 . DOI: 10.1109/tgrs.2020.2981997 http://dx.doi.org/10.1109/tgrs.2020.2981997

Chen J Z , Wang S H , Chen L , et al . Incremental detection of remote sensing objects with feature pyramid and knowledge distillation [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2022 , 60 : 5600413 . DOI: 10.1109/tgrs.2020.3042554 http://dx.doi.org/10.1109/tgrs.2020.3042554

Gu S S , Sun X Y , Yang Z H , et al . Energy-aware coded caching strategy design with resource optimization for satellite-UAV-vehicle-integrated networks [J ] . IEEE Internet of Things Journal , 2022 , 9 ( 8 ): 5799 - 5811 . DOI: 10.1109/jiot.2021.3065664 http://dx.doi.org/10.1109/jiot.2021.3065664

Wang L Y , Hu P , Zheng H W , et al . Integrative modeling of heterogeneous soil salinity using sparse ground samples and remote sensing images [J ] . Geoderma , 2023 , 430 : 116321 . DOI: 10.1016/j.geoderma.2022.116321 http://dx.doi.org/10.1016/j.geoderma.2022.116321

Chen B W , Chen K Y , Yang M H , et al . Heterogeneous mixture of experts for remote sensing image super-resolution [J ] . IEEE Geoscience and Remote Sensing Letters , 2025 , 22 : 6006405 . DOI: 10.1109/lgrs.2025.3557928 http://dx.doi.org/10.1109/lgrs.2025.3557928

Razakarivony S , Jurie F . Vehicle detection in aerial imagery: A small target detection benchmark [J ] . Journal of Visual Communication and Image Representation , 2016 , 34 : 187 - 203 . DOI: 10.1016/j.jvcir.2015.11.002 http://dx.doi.org/10.1016/j.jvcir.2015.11.002

Liu Z K , Yuan L , Weng L B , et al . A high resolution optical satellite image dataset for ship recognition and some new baselines [C ] // Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods . SCITEPRESS - Science and Technology Publications . Porto : SCITEPRESS , 2017 : 324 - 331 . DOI: 10.5220/0006120603240331 http://dx.doi.org/10.5220/0006120603240331

Long Y , Gong Y P , Xiao Z F , et al . Accurate object localization in remote sensing images based on convolutional neural networks [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2017 , 55 ( 5 ): 2486 - 2498 . DOI: 10.1109/tgrs.2016.2645610 http://dx.doi.org/10.1109/tgrs.2016.2645610

Ding J , Xue N , Xia G S , et al . Object detection in aerial images: A large-scale benchmark and challenges [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2022 , 44 ( 11 ): 7778 - 7796 . DOI: 10.1109/tpami.2021.3117983 http://dx.doi.org/10.1109/tpami.2021.3117983

Lam D , Kuzma R , McGee K , et al . xView: Objects in context in overhead imagery [PP/OL ] . V1.arXiv ( 2018-02-22 )[ 2025-12-11 ] . https://doi.org/10.48550/arXiv.1802.07856 https://doi.org/10.48550/arXiv.1802.07856 .

Zhu P F , Wen L Y , Du D W , et al . Detection and tracking meet drones challenge [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2022 , 44 ( 11 ): 7380 - 7399 . DOI: 10.1109/tpami.2021.3119563 http://dx.doi.org/10.1109/tpami.2021.3119563

Sun X , Wang P J , Yan Z Y , et al . FAIR1M: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery [J ] . ISPRS Journal of Photogrammetry and Remote Sensing , 2022 , 184 : 116 - 130 . DOI: 10.1016/j.isprsjprs.2021.12.004 http://dx.doi.org/10.1016/j.isprsjprs.2021.12.004

Zhang T , Zhang X , Li J , et al . SAR ship detection dataset (SSDD): Official release and comprehensive data analysis [J ] . Remote Sensing , 2021 , 13 ( 18 ): 3690 . DOI: 10.3390/rs13183690 http://dx.doi.org/10.3390/rs13183690

Xiao M , He Z , Li X Y , et al . Power transformations and feature alignment guided network for SAR ship detection [J ] . IEEE Geoscience and Remote Sensing Letters , 2022 , 19 : 4509405 . DOI: 10.1109/lgrs.2022.3183832 http://dx.doi.org/10.1109/lgrs.2022.3183832

徐从安 , 苏航 , 李健伟 , 等 . RSDD-SAR:SAR舰船斜框检测数据集 [J ] . 雷达学报 , 2022 , 11 ( 4 ): 581 - 599 .

Xu Congan , Su Hang , Li Jianwei , et al . RSDD-SAR: Rotated ship detection dataset in SAR images [J ] . Journal of Radars , 2022 , 11 ( 4 ): 581 - 599 . (in Chinese)

Zhang X , Yang X , Li Y X , et al . RSAR: Restricted state angle resolver and rotated SAR benchmark [PP/OL ] . V1. arXiv ( 2025-01-08 )[ 2025-12-11 ] . https://doi.org/10.48550/arXiv.2501.04440 https://doi.org/10.48550/arXiv.2501.04440 .

Bondi E , Jain R , Aggrawal P , et al . BIRDSAI: A dataset for detection and tracking in aerial thermal infrared videos [C ] // 2020 IEEE Winter Conference on Applications of Computer Vision . Piscataway : IEEE , 2020 : 1736 - 1745 . DOI: 10.1109/wacv45572.2020.9093284 http://dx.doi.org/10.1109/wacv45572.2020.9093284

Dai Y M , Wu Y Q , Zhou F , et al . Asymmetric contextual modulation for infrared small target detection [C ] // 2021 IEEE Winter Conference on Applications of Computer Vision . Piscataway : IEEE , 2021 : 949 - 958 . DOI: 10.1109/wacv48630.2021.00099 http://dx.doi.org/10.1109/wacv48630.2021.00099

Lu J H , Liu D S , Cheng X , et al . An efficient unstructured sparse convolutional neural network accelerator for wearable ECG classification device [J ] . IEEE Transactions on Circuits and Systems I: Regular Papers , 2022 , 69 ( 11 ): 4572 - 4582 . DOI: 10.1109/tcsi.2022.3194636 http://dx.doi.org/10.1109/tcsi.2022.3194636

Hu Z P , Gong M G , Lu Y H , et al . Data customization-based multiobjective optimization pruning framework for remote sensing scene classification [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2024 , 62 : 4508514 . DOI: 10.1109/tgrs.2023.3320650 http://dx.doi.org/10.1109/tgrs.2023.3320650

Xie Y F , Hou X , Ren J C , et al . Binary quantization vision transformer for effective segmentation of red tide in multispectral remote sensing imagery [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2025 , 63 : 4202814 . DOI: 10.1109/tgrs.2025.3540784 http://dx.doi.org/10.1109/tgrs.2025.3540784

Wang R Q , Fu Y F , Xu Y , et al . Dual prediction-guided distillation for object detection in remote sensing images [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2025 , 63 : 5618817 . DOI: 10.1109/tgrs.2025.3553128 http://dx.doi.org/10.1109/tgrs.2025.3553128

Yang Y R , Sun X , Diao W H , et al . Adaptive knowledge distillation for lightweight remote sensing object detectors optimizing [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2022 , 60 : 5623715 . DOI: 10.1109/tgrs.2022.3175213 http://dx.doi.org/10.1109/tgrs.2022.3175213

Yang X , Zhang S , Yang W C . Two-way assistant: A knowledge distillation object detection method for remote sensing images [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2024 , 62 : 5612710 . DOI: 10.1109/tgrs.2024.3371681 http://dx.doi.org/10.1109/tgrs.2024.3371681

Wang L Y , Zhang J , Yao J C , et al . Porn streamer recognition in live video based on multimodal knowledge distillation [J ] . Chinese Journal of Electronics , 2021 , 30 ( 6 ): 1096 - 1102 . DOI: 10.1049/cje.2021.07.027 http://dx.doi.org/10.1049/cje.2021.07.027

王立元 . 基于多模态深度学习的网络直播视频不良主播识别技术与实现 [D ] . 北京 : 北京工业大学 , 2020 .

Wang Liyuan . Identification technology and implementation of bad anchor in webcast video based on multimodal deep learning [D ] . Beijing : Beijing University of Technology , 2020 . (in Chinese)

Han L R , Paoletti M E , Moreno-álvarez S , et al . Deep robust hashing using self-distillation for remote sensing image retrieval [C ] // IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium . Piscataway : IEEE , 2024 : 7111 - 7114 . DOI: 10.1109/igarss53475.2024.10641118 http://dx.doi.org/10.1109/igarss53475.2024.10641118

Hu Y T , Zhang L , Luo X Y , et al . Diffusion self-distillation for remote sensing scene classification [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2025 , 63 : 5626315 . DOI: 10.1109/tgrs.2025.3569616 http://dx.doi.org/10.1109/tgrs.2025.3569616

Wang L Q , Zhang J , Tian J M , et al . Efficient fine-grained object recognition in high-resolution remote sensing images from knowledge distillation to filter grafting [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2023 , 61 : 4701016 . DOI: 10.1109/tgrs.2023.3260883 http://dx.doi.org/10.1109/tgrs.2023.3260883

田吉淼 . 高分辨率遥感影像的细粒度目标识别技术实现 [D ] . 北京 : 北京工业大学 , 2022 .

Tian Jimiao . Realization of fine-grained target recognition technology for high-resolution remote sensing images [D ] . Beijing : Beijing University of Technology , 2022 . (in Chinese)

Hou L P , Lu K , Xue J , et al . Shape-adaptive selection and measurement for oriented object detection [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2022 , 36 ( 1 ): 923 - 932 . DOI: 10.1609/aaai.v36i1.19975 http://dx.doi.org/10.1609/aaai.v36i1.19975

Han J M , Ding J , Li J , et al . Align deep features for oriented object detection [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2022 , 60 : 5602511 . DOI: 10.1109/tgrs.2021.3062048 http://dx.doi.org/10.1109/tgrs.2021.3062048

Tian J M , Zhang J , Li W S , et al . Structurally re-parameterized rotation detector for arbitrary-oriented objects in high-resolution remote sensing images [J ] . International Journal of Remote Sensing , 2022 , 43 ( 1 ): 241 - 269 . DOI: 10.1080/01431161.2021.2012294 http://dx.doi.org/10.1080/01431161.2021.2012294

Kingma D P , Salimans T , Poole B , et al . Variational diffusion models [C ] // Proceedings of the 35th International Conference on Neural Information Processing Systems . New York : ACM , 2021 : 21696 - 21707 .

Xie X X , Cheng G , Wang J B , et al . Oriented R-CNN for object detection [C ] // 2021 IEEE/CVF International Conference on Computer Vision . Piscataway : IEEE , 2021 : 3500 - 3509 . DOI: 10.1109/iccv48922.2021.00350 http://dx.doi.org/10.1109/iccv48922.2021.00350

Wang L Q , Zhang J , Mi G M , et al . Weak supervision makes strong details: Fine-grained object recognition in remote sensing images via regional diffusion with VLM [J ] . ISPRS Journal of Photogrammetry and Remote Sensing , 2026 , 233 : 231 - 246 . DOI: 10.1016/j.isprsjprs.2026.01.024 http://dx.doi.org/10.1016/j.isprsjprs.2026.01.024

Sun X , Yu Y H , Cheng Q . Low-rank multimodal remote sensing object detection with frequency filtering experts [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2024 , 62 : 5637114 . DOI: 10.1109/tgrs.2024.3446814 http://dx.doi.org/10.1109/tgrs.2024.3446814

Yao J C , Zhang J , Zhang H , et al . LCMA-Net: A light cross-modal attention network for streamer re-identification in live video [J ] . Computer Vision and Image Understanding , 2024 , 249 : 104183 . DOI: 10.1016/j.cviu.2024.104183 http://dx.doi.org/10.1016/j.cviu.2024.104183

Wang L Q , Li J F , Zhang J , et al . Position guided dynamic receptive field network: A small object detection friendly to optical and SAR images [J ] . IEEE Transactions on Circuits and Systems for Video Technology , 2025 , 35 ( 8 ): 8265 - 8282 . DOI: 10.1109/tcsvt.2025.3547436 http://dx.doi.org/10.1109/tcsvt.2025.3547436

Li Y X , Hou Q B , Zheng Z H , et al . Large selective kernel network for remote sensing object detection [C ] // 2023 IEEE/CVF International Conference on Computer Vision . Piscataway : IEEE , 2023 : 16748 - 16759 . DOI: 10.1109/iccv51070.2023.01540 http://dx.doi.org/10.1109/iccv51070.2023.01540

Lyu C Q , Zhang W W , Huang H A , et al . RTMDet: An empirical study of designing real-time object detectors [PP/OL ] . V2.arXiv ( 2022-12-16 )[ 2025-12-11 ] . https://doi.org/10.48550/arXiv.2212.07784 https://doi.org/10.48550/arXiv.2212.07784 .

Howard A , Sandler M , Chen B , et al . Searching for MobileNetV3 [C ] // 2019 IEEE/CVF International Conference on Computer Vision . Piscataway : IEEE , 2019 : 1314 - 1324 . DOI: 10.1109/iccv.2019.00140 http://dx.doi.org/10.1109/iccv.2019.00140

Tan M , Le V Q . Efficientnet: Rethinking model scaling for convolutional neural networks [C ] // Proceedings of the 36th International Conference on Machine Learning . California : PMLR , 2019 : 6105 - 6114 .

Liang X , Zhang J , Tian Q , et al . A saliency guided shallow convolutional neural network for traffic signs retrieval [C ] // 2018 IEEE Conference on Multimedia Information Processing and Retrieval . Piscataway : IEEE , 2018 : 340 - 345 . DOI: 10.1109/mipr.2018.00076 http://dx.doi.org/10.1109/mipr.2018.00076

Dosovitskiy A , Beyer L , Kolesnikov A , et al . An image is worth 16x16 words: Transformers for image recognition at scale [PP/OL ] . V2.arXiv ( 2021-06-03 )[ 2025-12-11 ] . https://doi.org/10.48550/arXiv.2010.11929 https://doi.org/10.48550/arXiv.2010.11929 .

Liu Z , Lin Y T , Cao Y , et al . Swin transformer: Hierarchical vision transformer using shifted windows [C ] // 2021 IEEE/CVF International Conference on Computer Vision . Piscataway : IEEE , 2021 : 9992 - 10002 . DOI: 10.1109/iccv48922.2021.00986 http://dx.doi.org/10.1109/iccv48922.2021.00986

Mehta S , Rastegari M . MobileViT: Light-weight, general-purpose, and mobile-friendly vision transformer [PP/OL ] . V2.arXiv ( 2022-03-04 )[ 2025-12-11 ] . https://doi.org/10.48550/arXiv.2110.02178 https://doi.org/10.48550/arXiv.2110.02178 .

Chu X , Tia Z , Wang Y , et al . Twins: Revisiting the design of spatial attention in vision transformers [C/OL ] // Proceedings of the 35th International Conference on Neural Information Processing Systems . Virtual Event: Curran Associates, Inc. , 2021 : 9355 - 9366 . https://dl.acm.org/doi/10.5555/3540261.3540977 https://dl.acm.org/doi/10.5555/3540261.3540977 .

Zhang W , Cai M X , Zhang T , et al . EarthMarker: A visual prompting multimodal large language model for remote sensing [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2025 , 63 : 5604219 . DOI: 10.1109/tgrs.2024.3523505 http://dx.doi.org/10.1109/tgrs.2024.3523505

Liu K W , Peng D L , Li T . Multimodal remote sensing object detection based on prior-enhanced mixture-of-experts fusion network [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2025 , 63 : 5405314 . DOI: 10.1109/tgrs.2025.3585634 http://dx.doi.org/10.1109/tgrs.2025.3585634

Lin Q F , Zhao J H , Du B , et al . MEDNet: Multiexpert detection network with unsupervised clustering of training samples [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2022 , 60 : 4703114 . DOI: 10.1109/tgrs.2021.3093556 http://dx.doi.org/10.1109/tgrs.2021.3093556

Qiu J L , Kuang Z F , Huang Z Q , et al . Security offloading scheduling and caching optimization algorithm in UAV edge computing [J ] . IEEE Systems Journal , 2025 , 19 ( 1 ): 96 - 106 . DOI: 10.1109/jsyst.2025.3531837 http://dx.doi.org/10.1109/jsyst.2025.3531837

Kim M , Hong S , Ko R , et al . Oaken: Fast and efficient LLM serving with online-offline hybrid KV cache quantization [PP/OL ] . V2.arXiv ( 2025-05-14 )[ 2025-12-11 ] . https://doi.org/10.48550/arXiv.2503.18599 https://doi.org/10.48550/arXiv.2503.18599 .

Li B Y , Wang L G , Wang Y Q , et al . Mixed-precision network quantization for infrared small target segmentation [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2024 , 62 : 5000812 . DOI: 10.1109/tgrs.2023.3346904 http://dx.doi.org/10.1109/tgrs.2023.3346904

曹峰 , 曾科文 , 李德玉 , 等 . 基于EIMYOLO的高分遥感图像目标检测 [J ] . 电子学报 , 2025 , 53 ( 7 ): 2266 - 2278 .

Cao Feng , Zeng Kewen , Li Deyu , et al . Object detection based on EIMYOLO for high-resolution remote sensing images [J ] . Acta Electronica Sinica , 2025 , 53 ( 7 ): 2266 - 2278 . (in Chinese)

Lin H , Hong D F , Ge S H , et al . RS-MoE: A vision-language model with mixture of experts for remote sensing image captioning and visual question answering [PP/OL ] . V2.arXiv ( 2025-02-10 )[ 2025-12-11 ] . https://doi.org/10.48550/arXiv.2411.01595 https://doi.org/10.48550/arXiv.2411.01595 .

秦董洪 , 李政韬 , 白凤波 , 等 . 大语言模型参数高效微调技术综述 [J ] . 计算机工程与应用 , 2025 , 61 ( 16 ): 38 - 63 .

Qin Donghong , Li Zhengtao , Bai Fengbo , et al . Review of parameter-efficient fine-tuning technology for large language models [J ] . Computer Engineering and Applications , 2025 , 61 ( 16 ): 38 - 63 . (in Chinese)

Hu E J , Shen Y , Wallis P , et al . LoRA: Low-rank adaptation of large language models [C ] // International Conference on Learning Representations . Virtual Event : ICLR , 2022 .

Dettmers T , Holtzman A , Pagnoni A , et al . QLoRA: Efficient finetuning of quantized LLMs [C ] // Advances in Neural Information Processing Systems 36 . Neural Information Processing Systems Foundation, Inc. (NeurIPS) , 2023 : 10088 - 10115 . DOI: 10.52202/075280-0441 http://dx.doi.org/10.52202/075280-0441

Chen Y K , Qian S J , Tang H T , et al . LongLoRA: Efficient fine-tuning of long-context large language models [PP/OL ] . V3.arXiv ( 2024-03-08 )[ 2025-12-11 ] . https://doi.org/10.48550/arXiv.2309.12307 https://doi.org/10.48550/arXiv.2309.12307 .

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