
%% Journal article

%%============================================================================%%
%% while using chicago reference style, both abbreviated and expanded form of %%
%% author name format is acceptable. Refer below example for expanded form    %%
%%============================================================================%%

%%  author		= "{Cameron, Deborah}", - single author
%%  author		= "{Saito, Yukio} and {Hyuga, Hiroyuki}", - double author 

%%======================================%%
%% Example for author names with suffix %%
%%======================================%%

%%  author		= "{Price, R. A. Jr} and {Curry, N. {III}} and McCann, K. E. and 
%%					Fielding, J. L. and {Abercrombie, E. Jr}",

@article{pca_classification,
  author={Azam, Faisal and Rizvi, Akash and Khan, Wazir Zada and Aalsalem, Mohammed Y. and Yu, Heejung and Zikria, Yousaf Bin},
  journal={IEEE Access}, 
  title={Aircraft Classification Based on PCA and Feature Fusion Techniques in Convolutional Neural Network}, 
  year={2021},
  volume={9},
  number={},
  pages={161683-161694},
  keywords={Feature extraction;Convolutional neural networks;Remote sensing;Principal component analysis;Airplanes;Satellites;Support vector machines;Aircraft classification;CNN;feature extraction;feature fusion;identification of aircraft},
  doi={10.1109/ACCESS.2021.3132062}}

@article{backscattering,
  author={Chen, Jiehong and Zhang, Bo and Wang, Chao},
  journal={IEEE Geoscience and Remote Sensing Letters}, 
  title={Backscattering Feature Analysis and Recognition of Civilian Aircraft in TerraSAR-X Images}, 
  year={2015},
  volume={12},
  number={4},
  pages={796-800},
  keywords={Scattering;Synthetic aperture radar;Feature extraction;Image recognition;Detectors;Remote sensing;Backscatter;Civilian aircraft (CA);recognition;salient point;synthetic aperture radar (SAR);Civilian aircraft (CA);recognition;salient point;synthetic aperture radar (SAR)},
  doi={10.1109/LGRS.2014.2362845}}

@article{segmentation_recognition,
  author={Zuo, Jiawei and Xu, Guangluan and Fu, Kun and Sun, Xian and Sun, Hao},
  journal={IEEE Geoscience and Remote Sensing Letters}, 
  title={Aircraft Type Recognition Based on Segmentation With Deep Convolutional Neural Networks}, 
  year={2018},
  volume={15},
  number={2},
  pages={282-286},
  keywords={Aircraft;Image segmentation;Atmospheric modeling;Aircraft manufacture;Shape;Estimation;Aircraft type recognition;deep convolutional neural networks (CNNs);image segmentation;keypoints’ detection},
  doi={10.1109/LGRS.2017.2786232}}

@article{yolo_extract,
  author={Liu, Zhiguo and Gao, Yuan and Du, Qianqian},
  journal={IEEE Access}, 
  title={YOLO-Class: Detection and Classification of Aircraft Targets in Satellite Remote Sensing Images Based on YOLO-Extract}, 
  year={2023},
  volume={11},
  number={},
  pages={109179-109188},
  keywords={Remote sensing;Aircraft;Feature extraction;Atmospheric modeling;Computational modeling;Training;Satellites;Satellite remote sensing images;YOLO-extract;representative batch normalization;VariFocal loss;RepVGG},
  doi={10.1109/ACCESS.2023.3321828}}

@article{bakirman2022benchmark,
   title={A benchmark dataset for deep learning-based airplane detection: HRPlanes},
   volume={8},
   ISSN={2548-0960},
   url={http://dx.doi.org/10.26833/ijeg.1107890},
   DOI={10.26833/ijeg.1107890},
   number={3},
   journal={International Journal of Engineering and Geosciences},
   publisher={International Journal of Engineering and Geoscience},
   author={Bak{\i}rman, Tolga and Sertel, Elif},
   year={2023},
   month=oct, pages={212–223}
}

@article{alkharji,
Author = {Alkharji, Noora and Almazrouei, Hessa and Alzaabi, Shaikha and Nassif, Ali Bou and Elsalhy, Mariame and Talib, Manar Abu},
ISSN = {979-8-3503-5098-2},
Journal = {2024 IEEE 12th International Conference on Intelligent Systems (IS), Intelligent Systems (IS), 2024 IEEE 12th International Conference on},
Pages = {1 - 6},
Title = {Aircraft-Type Classification Using Deep Learning Algorithms.},
Year = {2024},
}

@article{strohmeier,
  title={Classi-fly: Inferring aircraft categories from open data},
  author={Strohmeier, Martin and Smith, Matthew and Lenders, Vincent and Martinovic, Ivan},
  journal={ACM Transactions on Intelligent Systems and Technology (TIST)},
  volume={12},
  number={6},
  pages={1--23},
  year={2021},
  publisher={ACM New York, NY}
}

@article{flightscope,
  title={FlightScope: An Experimental Comparative Review of Aircraft Detection Algorithms in Satellite Imagery},
  author={El Ghazouali, Safouane and Gucciardi, Arnaud and Venturini, Francesca and Venturi, Nicola and Rueegsegger, Michael and Michelucci, Umberto},
  journal={Remote Sensing},
  volume={16},
  number={24},
  pages={4715},
  year={2024},
  publisher={MDPI}
}

@article{bakirci,
Author = {Bakirci, Murat and Bayraktar, Irem},
ISSN = {979-8-3315-4010-4},
Journal = {2024 8th International Symposium on Innovative Approaches in Smart Technologies (ISAS), Innovative Approaches in Smart Technologies (ISAS), 2024 8th International Symposium on},
Pages = {1 - 6},
Title = {The Cutting-Edge YOLO11 for Advanced Aircraft Detection in Synthetic Aperture Radar (SAR) Imagery.},
Year = {2024},
}

@misc{RarePlanes_Dataset,
    title={RarePlanes Dataset},
    author={Shermeyer, Jacob and Hossler, Thomas and Van Etten, Adam and Hogan, Daniel and Lewis, Ryan and Kim, Daeil},
    organization = {In-Q-Tel - CosmiQ Works and AI.Reverie},
    month = {June},
    year = {2020}
}

@online{rareplanes_aws,
  title        = {RarePlanes},
  year         = {2025},
  howpublished = {\url{https://registry.opendata.aws/rareplanes}},
  note         = {Accessed on: 01/02/2025}
}

@article{RarePlanes_Paper,
    title={RarePlanes: Synthetic Data Takes Flight},
    author={Shermeyer, Jacob and Hossler, Thomas and Van Etten, Adam and Hogan, Daniel and Lewis, Ryan and Kim, Daeil},
    organization = {In-Q-Tel - CosmiQ Works and AI.Reverie},
    month = {June},
    year = {2020}
}

@misc{hrplanesv2,
  author = {Unsal, D.},
  year = {2022},
  title = {HRPlanesv2 - High Resolution Satellite Imagery for Aircraft Detection [Data set]},
  publisher = {Zenodo},
  doi = {10.5281/zenodo.7331974},
  url = {https://doi.org/10.5281/zenodo.7331974}
}

@article{khan,
author = {Khan, Sajid Ullah and Alharbi, Meshal and Shah, Sajid and ELAffendi, Mohammed},
title = {Medical Image Fusion for Multiple Diseases Features Enhancement},
journal = {International Journal of Imaging Systems and Technology},
volume = {34},
number = {6},
pages = {e23197},
keywords = {anatomical, functional information, MDcGAN, multimodal medical image fusion, structural, structure feature adjustment},
doi = {https://doi.org/10.1002/ima.23197},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/ima.23197},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/ima.23197},
note = {e23197 IMA-24-928.R1},
year = {2024}
}

@INPROCEEDINGS{zhang,
  author={Zhang, Jianlong and Wang, Tianhong and Wang, Bin and Chen, Chen},
  booktitle={2022 IEEE International Conference on Smart Internet of Things (SmartIoT)}, 
  title={A Subspace Fusion of Hyper-parameter Optimization Method Based on Mean Regression}, 
  year={2022},
  volume={},
  number={},
  pages={169-174},
  keywords={Graphical models;Computational modeling;Neural networks;Optimization methods;Object detection;Search problems;Data models;hyper-parameter optimization;mean regression;missed detection space},
  doi={10.1109/SmartIoT55134.2022.00035}
}

@misc{redmon,
      title={YOLOv3: An Incremental Improvement}, 
      author={Joseph Redmon and Ali Farhadi},
      year={2018},
      eprint={1804.02767},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/1804.02767}
}

@misc{ren,
      title={Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks}, 
      author={Shaoqing Ren and Kaiming He and Ross Girshick and Jian Sun},
      year={2016},
      eprint={1506.01497},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/1506.01497}, 
}

@inproceedings{redmon2016you,
  title={You Only Look Once: Unified, Real-Time Object Detection},
  author={Redmon, Joseph and Divvala, Santosh and Girshick, Ross and Farhadi, Ali},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages={779--788},
  year={2016}
}

@inbook{Liu_2016,
   title={SSD: Single Shot MultiBox Detector},
   ISBN={9783319464480},
   ISSN={1611-3349},
   url={http://dx.doi.org/10.1007/978-3-319-46448-0_2},
   DOI={10.1007/978-3-319-46448-0_2},
   booktitle={Computer Vision – ECCV 2016},
   publisher={Springer International Publishing},
   author={Liu, Wei and Anguelov, Dragomir and Erhan, Dumitru and Szegedy, Christian and Reed, Scott and Fu, Cheng-Yang and Berg, Alexander C.},
   year={2016},
   pages={21--37} 
}

@misc{ultralytics,
  author = {Glenn Jocher and Jing Qiu},
  title = {Ultralytics YOLO11},
  version = {11.0.0},
  year = {2024},
  url = {https://github.com/ultralytics/ultralytics},
  license = {AGPL-3.0}
}

@misc{yolo-object-features,
    title = {Retrieving Object-Level Features From YOLO},
    author = {Yasin, Mohammed},
    date = {2024-10-17},
    abstract = {Extract the object-level features from YOLO for downstream tasks such as similarity calculation without the overhead of using a separate embedding network.},
    url = {https://y-t-g.github.io/tutorials/yolo-object-features/},
    type = {Blog},
    maintitle = {Yasin's Keep}
}

@article{traffic,
author = {Dave, Pritul and Chandarana, Arjun and Goel, Parth and Ganatra, Amit},
year = {2021},
month = {06},
pages = {e586},
title = {An amalgamation of YOLOv4 and XGBoost for next-gen smart traffic management system},
volume = {7},
journal = {PeerJ Computer Science},
doi = {10.7717/peerj-cs.586}
}

@article{metadata_animal,
author = {Aslak Tøn and Ammar Ahmed and Ali Shariq Imran and Mohib Ullah and R. Muhammad Atif Azad},
title = {Metadata augmented deep neural networks for wild animal classification},
journal = {Ecological Informatics},
year = {2024},
volume = {83},
publisher = {Elsevier},
month = {nov},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1574954124003479},
pages = {102805},
doi = {10.1016/j.ecoinf.2024.102805}
}

@misc{bock,
      title={YOLOv4: Optimal Speed and Accuracy of Object Detection}, 
      author={Alexey Bochkovskiy and Chien-Yao Wang and Hong-Yuan Mark Liao},
      year={2020},
      eprint={2004.10934},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2004.10934}
}

@article{shorten2019,
  title={A survey on Image Data Augmentation for Deep Learning},
  author={Shorten, Connor and Khoshgoftaar, Taghi M},
  journal={Journal of Big Data},
  volume={6},
  number={1},
  pages={1--48},
  year={2019},
  publisher={Springer},
  doi={10.1186/s40537-019-0197-0},
  url={https://doi.org/10.1186/s40537-019-0197-0}
}

@inproceedings{optuna_2019,
    title={Optuna: A Next-generation Hyperparameter Optimization Framework},
    author={Akiba, Takuya and Sano, Shotaro and Yanase, Toshihiko and Ohta, Takeru and Koyama, Masanori},
    booktitle={Proceedings of the 25th {ACM} {SIGKDD} International Conference on Knowledge Discovery and Data Mining},
    year={2019}
}

@INPROCEEDINGS{yolo_math,
  author={Liu, Chengji and Tao, Yufan and Liang, Jiawei and Li, Kai and Chen, Yihang},
  booktitle={2018 IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC)}, 
  title={Object Detection Based on YOLO Network}, 
  year={2018},
  volume={},
  number={},
  pages={799-803},
  keywords={Standards;Degradation;Training;Object detection;Data models;Neural networks;Classification algorithms;YOLO network;image processing;object detection},
  doi={10.1109/ITOEC.2018.8740604}}

@misc{maji,
      title={Fine-Grained Visual Classification of Aircraft}, 
      author={Subhransu Maji and Esa Rahtu and Juho Kannala and Matthew Blaschko and Andrea Vedaldi},
      year={2013},
      eprint={1306.5151},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/1306.5151}, 
}

@article{docker,
  title={Docker: lightweight linux containers for consistent development and deployment},
  author={Merkel, Dirk},
  journal={Linux journal},
  volume={2014},
  number={239},
  pages={2},
  year={2014}
}

@book{van1995python, 
  title={The Python Library Reference, release 3.8.2},
  author={Van Rossum, Guido}, 
  year={2020}, 
  publisher={Python Software Foundation} 
}

@incollection{torch,
title = {PyTorch: An Imperative Style, High-Performance Deep Learning Library},
author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
booktitle = {Advances in Neural Information Processing Systems 32},
pages = {8024--8035},
year = {2019},
publisher = {Curran Associates, Inc.},
url = {http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf}
}

@Article{numpy,
 title         = {Array programming with {NumPy}},
 author        = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J.
                 van der Walt and Ralf Gommers and Pauli Virtanen and David
                 Cournapeau and Eric Wieser and Julian Taylor and Sebastian
                 Berg and Nathaniel J. Smith and Robert Kern and Matti Picus
                 and Stephan Hoyer and Marten H. van Kerkwijk and Matthew
                 Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del
                 R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre
                 G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and
                 Warren Weckesser and Hameer Abbasi and Christoph Gohlke and
                 Travis E. Oliphant},
 year          = {2020},
 month         = sep,
 journal       = {Nature},
 volume        = {585},
 number        = {7825},
 pages         = {357--362},
 doi           = {10.1038/s41586-020-2649-2},
 publisher     = {Springer Science and Business Media {LLC}},
 url           = {https://doi.org/10.1038/s41586-020-2649-2}
}

@software{pandas,
    author       = {The pandas development team},
    title        = {pandas-dev/pandas: Pandas},
    month        = feb,
    year         = 2020,
    publisher    = {Zenodo},
    version      = {latest},
    doi          = {10.5281/zenodo.3509134},
    url          = {https://doi.org/10.5281/zenodo.3509134}
}

@misc{tensorflow,
title={{TensorFlow}: Large-Scale Machine Learning on Heterogeneous Systems},
url={http://tensorflow.org/},
note={Software available from tensorflow.org},
author={
    Mart\'{\i}n~Abadi and
    Ashish~Agarwal and
    Paul~Barham and
    Eugene~Brevdo and
    Zhifeng~Chen and
    Craig~Citro and
    Greg~S.~Corrado and
    Andy~Davis and
    Jeffrey~Dean and
    Matthieu~Devin and
    Sanjay~Ghemawat and
    Ian~Goodfellow and
    Andrew~Harp and
    Geoffrey~Irving and
    Michael~Isard and
    Yangqing Jia and
    Rafal~Jozefowicz and
    Lukasz~Kaiser and
    Manjunath~Kudlur and
    Josh~Levenberg and
    Dan~Man\'{e} and
    Rajat~Monga and
    Sherry~Moore and
    Derek~Murray and
    Chris~Olah and
    Mike~Schuster and
    Jonathon~Shlens and
    Benoit~Steiner and
    Ilya~Sutskever and
    Kunal~Talwar and
    Paul~Tucker and
    Vincent~Vanhoucke and
    Vijay~Vasudevan and
    Fernanda~Vi\'{e}gas and
    Oriol~Vinyals and
    Pete~Warden and
    Martin~Wattenberg and
    Martin~Wicke and
    Yuan~Yu and
    Xiaoqiang~Zheng},
  year={2015},
}

@software{geopandas,
  author       = {Kelsey Jordahl and
                  Joris Van den Bossche and
                  Martin Fleischmann and
                  Jacob Wasserman and
                  James McBride and
                  Jeffrey Gerard and
                  Jeff Tratner and
                  Matthew Perry and
                  Adrian Garcia Badaracco and
                  Carson Farmer and
                  Geir Arne Hjelle and
                  Alan D. Snow and
                  Micah Cochran and
                  Sean Gillies and
                  Lucas Culbertson and
                  Matt Bartos and
                  Nick Eubank and
                  maxalbert and
                  Aleksey Bilogur and
                  Sergio Rey and
                  Christopher Ren and
                  Dani Arribas-Bel and
                  Leah Wasser and
                  Levi John Wolf and
                  Martin Journois and
                  Joshua Wilson and
                  Adam Greenhall and
                  Chris Holdgraf and
                  Filipe and
                  François Leblanc},
  title        = {geopandas/geopandas: v0.8.1},
  month        = jul,
  year         = 2020,
  publisher    = {Zenodo},
  version      = {v0.8.1},
  doi          = {10.5281/zenodo.3946761},
  url          = {https://doi.org/10.5281/zenodo.3946761}
}

@Article{mathplotlib,
  Author    = {Hunter, J. D.},
  Title     = {Matplotlib: A 2D graphics environment},
  Journal   = {Computing in Science \& Engineering},
  Volume    = {9},
  Number    = {3},
  Pages     = {90--95},
  abstract  = {Matplotlib is a 2D graphics package used for Python for
  application development, interactive scripting, and publication-quality
  image generation across user interfaces and operating systems.},
  publisher = {IEEE COMPUTER SOC},
  doi       = {10.1109/MCSE.2007.55},
  year      = 2007
}

@Misc{shapely,
  author = {Sean Gillies and others},
  organization = {toblerity.org},
  title = {Shapely: manipulation and analysis of geometric objects},
  year =  {2007--},
  url = "https://github.com/Toblerity/Shapely"
}

@inproceedings{sklearn_api,
  author    = {Lars Buitinck and Gilles Louppe and Mathieu Blondel and
                Fabian Pedregosa and Andreas Mueller and Olivier Grisel and
                Vlad Niculae and Peter Prettenhofer and Alexandre Gramfort
                and Jaques Grobler and Robert Layton and Jake VanderPlas and
                Arnaud Joly and Brian Holt and Ga{\"{e}}l Varoquaux},
  title     = {{API} design for machine learning software: experiences from the scikit-learn
                project},
  booktitle = {ECML PKDD Workshop: Languages for Data Mining and Machine Learning},
  year      = {2013},
  pages = {108--122},
}

@article{scikit-image,
 title = {scikit-image: image processing in {P}ython},
 author = {van der Walt, {S}t\'efan and {S}ch\"onberger, {J}ohannes {L}. and
           {Nunez-Iglesias}, {J}uan and {B}oulogne, {F}ran\c{c}ois and {W}arner,
           {J}oshua {D}. and {Y}ager, {N}eil and {G}ouillart, {E}mmanuelle and
           {Y}u, {T}ony and the scikit-image contributors},
 year = {2014},
 month = {6},
 keywords = {Image processing, Reproducible research, Education,
             Visualization, Open source, Python, Scientific programming},
 volume = {2},
 pages = {e453},
 journal = {PeerJ},
 issn = {2167-8359},
 url = {https://doi.org/10.7717/peerj.453},
 doi = {10.7717/peerj.453}
}

@inproceedings{xgboost,
author = {Chen, Tianqi and Guestrin, Carlos},
title = {XGBoost: A Scalable Tree Boosting System},
year = {2016},
isbn = {9781450342322},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2939672.2939785},
doi = {10.1145/2939672.2939785},
abstract = {Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.},
booktitle = {Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {785–794},
numpages = {10},
keywords = {large-scale machine learning},
location = {San Francisco, California, USA},
series = {KDD '16}
}
@inproceedings{optuna,
    title={Optuna: A Next-generation Hyperparameter Optimization Framework},
    author={Akiba, Takuya and Sano, Shotaro and Yanase, Toshihiko and Ohta, Takeru and Koyama, Masanori},
    booktitle={Proceedings of the 25th {ACM} {SIGKDD} International Conference on Knowledge Discovery and Data Mining},
    year={2019}
}

@software{joblib,
title = {Joblib: running Python functions as pipeline jobs},
author = {{Joblib Development Team}},
year = {2020},
url = {https://joblib.readthedocs.io/},
}

@article{seaborn,
    doi = {10.21105/joss.03021},
    url = {https://doi.org/10.21105/joss.03021},
    year = {2021},
    publisher = {The Open Journal},
    volume = {6},
    number = {60},
    pages = {3021},
    author = {Michael L. Waskom},
    title = {seaborn: statistical data visualization},
    journal = {Journal of Open Source Software}
 }

@manual{python37,
  title={{Python: A dynamic, open source programming language}},
  author={{Python Core Team}},
  organization={{Python Software Foundation}},
  year={2019},
  url={https://www.python.org/},
  note={Python version 3.7}
}

@book{nltk,
  title={Natural Language Processing with Python},
  author={Bird, Steven, Edward Loper and Ewan Klein},
  year={2009},
  publisher={O'Reilly Media Inc.}
}

@manual{python,
  title={{Python: A dynamic, open source programming language}},
  author={{Python Core Team}},
  organization={{Python Software Foundation}},
  year={2019},
  url={https://www.python.org/},
}
@article{opencv,
    author = {Bradski, G.},
    citeulike-article-id = {2236121},
    journal = {Dr. Dobb's Journal of Software Tools},
    keywords = {bibtex-import},
    posted-at = {2008-01-15 19:21:54},
    priority = {4},
    title = {{The OpenCV Library}},
    year = {2000}
}

@article{imbalanced-learn,
author  = {Guillaume  Lema{{\^i}}tre and Fernando Nogueira and Christos K. Aridas},
title   = {Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning},
journal = {Journal of Machine Learning Research},
year    = {2017},
volume  = {18},
number  = {17},
pages   = {1-5},
url     = {http://jmlr.org/papers/v18/16-365.html}
}

@software{github_tt,
author = {Tandogan, Tugba},
title = {Aircraft\_Classification},
url = {https://github.com/tb-tan/Aircraft_Classification},
version = {1.0.0},
year = {2025}
}

@article{mtarsi,
title = {A benchmark data set for aircraft type recognition from remote sensing images},
journal = {Applied Soft Computing},
volume = {89},
pages = {106132},
year = {2020},
issn = {1568-4946},
doi = {https://doi.org/10.1016/j.asoc.2020.106132},
url = {https://www.sciencedirect.com/science/article/pii/S1568494620300727},
author = {Zhi-Ze Wu and Shou-Hong Wan and Xiao-Feng Wang and Ming Tan and Le Zou and Xin-Lu Li and Yan Chen}}

@article{AZAM2022104630,
title = {Aircraft detection in satellite imagery using deep learning-based object detectors},
journal = {Microprocessors and Microsystems},
volume = {94},
pages = {104630},
year = {2022},
issn = {0141-9331},
doi = {https://doi.org/10.1016/j.micpro.2022.104630},
url = {https://www.sciencedirect.com/science/article/pii/S0141933122001673},
author = {Basim Azam and Muhammad Jaleed Khan and Farrukh Aziz Bhatti and Abdur Rahman M. Maud and Syed Fawad Hussain and Ali Javed Hashmi and Khurram Khurshid},
keywords = {Aircraft detection, RCNN, YOLO, Resnet},
abstract = {Over the recent years, object detection in satellite imagery has become a crucial task in remote sensing applications. Specifically, the detection of aircraft is critical for military scenarios. Numerous computer vision techniques have been applied for this problem, however, robust and efficient detection of aircraft in satellite imagery poses several challenges, such as, variance of color, size, aspect ratio and orientation of aircraft and complex backgrounds. In this paper, we provide an in-depth review of deep learning methods for object detection applied to aircraft detection including both conventional and state-of-the-art techniques. We also provide a comprehensive experimental and comparative analysis of deep learning-based object detectors including Region-based CNN (RCNN), Fast RCNN, Faster RCNN and You Only Look Once (YOLO) encapsulating the most cited feature extraction networks including Alexnet, VGG-16, Resnet-18, Resnet-50, Resnet-101 and Inception-v3. The detailed quantitative comparison allows for selection of the most suitable set of object detector, backbone feature extraction network and hyperparameters for aircraft detection in satellite imagery with trade-off options between precision and detection time. The evaluation is based on mean average precision (mAP), precision versus recall, time complexity and computational complexity. Finally, we draw our conclusions and identify promising future work.}
}

@inbook{role_rs,
  author={Majumdar, Sushobhan},
  booktitle={Intelligent Data Analytics for Terror Threat Prediction: Architectures, Methodologies, Techniques, and Applications}, 
  title={The Role of Remote Sensing and GIS in Military Strategy to Prevent Terror Attacks}, 
  year={2021},
  volume={},
  number={},
  pages={79-94},
  keywords={Geographic information systems;Remote sensing;Terrorism;Satellites;Military communication;Software;Earth},
  doi={10.1002/9781119711629.ch4}}

@article{rs_diff,
title = {A survey on object detection in optical remote sensing images},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
volume = {117},
pages = {11-28},
year = {2016},
issn = {0924-2716},
doi = {https://doi.org/10.1016/j.isprsjprs.2016.03.014},
url = {https://www.sciencedirect.com/science/article/pii/S0924271616300144},
author = {Gong Cheng and Junwei Han},
keywords = {Object detection, Optical remote sensing images, Template matching, Object-based image analysis (OBIA), Machine learning, Deep learning, Weakly supervised learning},
abstract = {Object detection in optical remote sensing images, being a fundamental but challenging problem in the field of aerial and satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. While enormous methods exist, a deep review of the literature concerning generic object detection is still lacking. This paper aims to provide a review of the recent progress in this field. Different from several previously published surveys that focus on a specific object class such as building and road, we concentrate on more generic object categories including, but are not limited to, road, building, tree, vehicle, ship, airport, urban-area. Covering about 270 publications we survey (1) template matching-based object detection methods, (2) knowledge-based object detection methods, (3) object-based image analysis (OBIA)-based object detection methods, (4) machine learning-based object detection methods, and (5) five publicly available datasets and three standard evaluation metrics. We also discuss the challenges of current studies and propose two promising research directions, namely deep learning-based feature representation and weakly supervised learning-based geospatial object detection. It is our hope that this survey will be beneficial for the researchers to have better understanding of this research field.}
}

@Article{rs_diff_2,
AUTHOR = {Alganci, Ugur and Soydas, Mehmet and Sertel, Elif},
TITLE = {Comparative Research on Deep Learning Approaches for Airplane Detection from Very High-Resolution Satellite Images},
JOURNAL = {Remote Sensing},
VOLUME = {12},
YEAR = {2020},
NUMBER = {3},
ARTICLE-NUMBER = {458},
URL = {https://www.mdpi.com/2072-4292/12/3/458},
ISSN = {2072-4292},
ABSTRACT = {Object detection from satellite images has been a challenging problem for many years. With the development of effective deep learning algorithms and advancement in hardware systems, higher accuracies have been achieved in the detection of various objects from very high-resolution (VHR) satellite images. This article provides a comparative evaluation of the state-of-the-art convolutional neural network (CNN)-based object detection models, which are Faster R-CNN, Single Shot Multi-box Detector (SSD), and You Look Only Once-v3 (YOLO-v3), to cope with the limited number of labeled data and to automatically detect airplanes in VHR satellite images. Data augmentation with rotation, rescaling, and cropping was applied on the test images to artificially increase the number of training data from satellite images. Moreover, a non-maximum suppression algorithm (NMS) was introduced at the end of the SSD and YOLO-v3 flows to get rid of the multiple detection occurrences near each detected object in the overlapping areas. The trained networks were applied to five independent VHR test images that cover airports and their surroundings to evaluate their performance objectively. Accuracy assessment results of the test regions proved that Faster R-CNN architecture provided the highest accuracy according to the F1 scores, average precision (AP) metrics, and visual inspection of the results. The YOLO-v3 ranked as second, with a slightly lower performance but providing a balanced trade-off between accuracy and speed. The SSD provided the lowest detection performance, but it was better in object localization. The results were also evaluated in terms of the object size and detection accuracy manner, which proved that large- and medium-sized airplanes were detected with higher accuracy.},
DOI = {10.3390/rs12030458}
}

@article{rs_diff_3,
author = {Prudyus, Ivan and Tkachenko, Victor and Kondratov, Petro and Fabirovskyy, Sergiy and Lazko, L. and Hryvachevskyi, Andrii},
year = {2017},
month = {10},
pages = {},
title = {FACTORS AFFECTING THE QUALITY OF FORMATION AND RESOLUTION OF IMAGES IN REMOTE SENSING SYSTEMS},
volume = {5}
}
@INPROCEEDINGS{rcnn,
  author={Girshick, Ross},
  booktitle={2015 IEEE International Conference on Computer Vision (ICCV)}, 
  title={Fast R-CNN}, 
  year={2015},
  volume={},
  number={},
  pages={1440-1448},
  keywords={Training;Proposals;Feature extraction;Object detection;Pipelines;Computer architecture;Open source software},
  doi={10.1109/ICCV.2015.169}}

@article{one-stage,
  title={Deep Learning for Generic Object Detection: A Survey},
  author={Liu, Li and Ouyang, Wanli and Wang, Xiaogang and Fieguth, Paul and Chen, Jie and Liu, Xinwang and Pietikäinen, Matti},
  journal={International Journal of Computer Vision},
  volume={128},
  number={2},
  pages={261--318},
  year={2020},
  publisher={Springer}
}

@ARTICLE{one-stage_2,
    author={Hang Zhang and Rayan S Cloutier},
    title={Review on One-Stage Object Detection Based on Deep Learning},
    journal={EAI Endorsed Transactions on e-Learning},
    volume={7},
    number={23},
    publisher={EAI},
    journal_a={EL},
    year={2022},
    month={6},
    keywords={Object Detection, IoT, Deep learning, Computer Vision},
    doi={10.4108/eai.9-6-2022.174181}
}


@Article{conditionalGAN,
AUTHOR = {Zhang, Yuhang and Sun, Hao and Zuo, Jiawei and Wang, Hongqi and Xu, Guangluan and Sun, Xian},
TITLE = {Aircraft Type Recognition in Remote Sensing Images Based on Feature Learning with Conditional Generative Adversarial Networks},
JOURNAL = {Remote Sensing},
VOLUME = {10},
YEAR = {2018},
NUMBER = {7},
ARTICLE-NUMBER = {1123},
URL = {https://www.mdpi.com/2072-4292/10/7/1123},
ISSN = {2072-4292},
ABSTRACT = {Aircraft type recognition plays an important role in remote sensing image interpretation. Traditional methods suffer from bad generalization performance, while deep learning methods require large amounts of data with type labels, which are quite expensive and time-consuming to obtain. To overcome the aforementioned problems, in this paper, we propose an aircraft type recognition framework based on conditional generative adversarial networks (GANs). First, we design a new method to precisely detect aircrafts’ keypoints, which are used to generate aircraft masks and locate the positions of the aircrafts. Second, a conditional GAN with a region of interest (ROI)-weighted loss function is trained on unlabeled aircraft images and their corresponding masks. Third, an ROI feature extraction method is carefully designed to extract multi-scale features from the GAN in the regions of aircrafts. After that, a linear support vector machine (SVM) classifier is adopted to classify each sample using their features. Benefiting from the GAN, we can learn features which are strong enough to represent aircrafts based on a large unlabeled dataset. Additionally, the ROI-weighted loss function and the ROI feature extraction method make the features more related to the aircrafts rather than the background, which improves the quality of features and increases the recognition accuracy significantly. Thorough experiments were conducted on a challenging dataset, and the results prove the effectiveness of the proposed aircraft type recognition framework.},
DOI = {10.3390/rs10071123}
}


@Article{cntr_yolo,
AUTHOR = {Zhou, Fengyun and Deng, Honggui and Xu, Qiguo and Lan, Xin},
TITLE = {CNTR-YOLO: Improved YOLOv5 Based on ConvNext and Transformer for Aircraft Detection in Remote Sensing Images},
JOURNAL = {Electronics},
VOLUME = {12},
YEAR = {2023},
NUMBER = {12},
ARTICLE-NUMBER = {2671},
URL = {https://www.mdpi.com/2079-9292/12/12/2671},
ISSN = {2079-9292},
ABSTRACT = {Aircraft detection in remote sensing images is an important branch of target detection due to the military value of aircraft. However, the diverse categories of aircraft and the intricate background of remote sensing images often lead to insufficient detection accuracy. Here, we present the CNTR-YOLO algorithm based on YOLOv5 as a solution to this issue. The CNTR-YOLO algorithm improves detection accuracy through three primary strategies. (1) We deploy DenseNet in the backbone to address the vanishing gradient problem during training and enhance the extraction of fundamental information. (2) The CBAM attention mechanism is integrated into the neck to minimize background noise interference. (3) The C3CNTR module is designed based on ConvNext and Transformer to clarify the target’s position in the feature map from both local and global perspectives. This module is applied before the prediction head to optimize the accuracy of prediction results. Our proposed algorithm is validated on the MAR20 and DOTA datasets. The results on the MAR20 dataset show that the mean average precision (mAP) of CNTR-YOLO reached 70.1%, which is a 3.3% improvement compared with YOLOv5l. On the DOTA dataset, the results indicate that the mAP of CNTR-YOLO reached 63.7%, which is 2.5% higher than YOLOv5l.},
DOI = {10.3390/electronics12122671}
}

@ARTICLE{det-yolo,
  author={Chen, Xiaoxin and Jiang, Hui and Zheng, Hongxin and Yang, Jiankun and Liang, Riqiang and Xiang, Dan and Cheng, Hao and Jiang, Zhansi},
  journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}, 
  title={DET-YOLO: An Innovative High-Performance Model for Detecting Military Aircraft in Remote Sensing Images}, 
  year={2024},
  volume={17},
  number={},
  pages={17753-17771},
  keywords={Atmospheric modeling;Feature extraction;Military aircraft;Accuracy;Real-time systems;Computational modeling;Convolution;Attention mechanism;deep learning;deformable convolution (DCN);loss function;remote sensing images (RSIs);you only look once (YOLO)},
  doi={10.1109/JSTARS.2024.3462745}}

@inproceedings{improved_yolo11,
author = {Zhang, Chaofan and Wang, Ruiyang},
title = {Aircraft Detection Based on Improved YoloV11},
year = {2025},
isbn = {9798400713163},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3746709.3746872},
doi = {10.1145/3746709.3746872},
abstract = {With the increasing diversity and complexity of aircraft types, accurate and efficient target detection technology has become an important demand in the field of target recognition. This paper proposes a multi-class aircraft detection method based on improved YOLOv11. By introducing two key modules, ADown module and Multi-Scale Dilated Attention(MSDA), the robustness and computational efficiency of the model are significantly improved while ensuring detection accuracy. Firstly, in order to solve the problem that YOLOv11 may lose important features during the downsampling process, this paper introduces the ADown module. This module uses a more detailed feature processing method to optimize the model's downsampling process by dynamically adjusting and reconstructing different feature layers, thereby enhancing the model's ability to capture details in complex scenes. Secondly, combining the dilated convolution and Transformer structure, this paper introduces the Multi-Scale Dilated Attention(MSDA) module. This module effectively improves the detection accuracy of different types of aircraft in complex backgrounds by adaptively adjusting the attention mechanism at different scales, especially in the processing of shallow features and local details. In this paper, a multi-class dataset containing 77 different aircraft is used and compared with the traditional YOLOv11 model and other state-of-the-art target detection algorithms. The experimental results show that the improved YOLOv11 model outperforms the baseline model in multiple evaluation metrics and excels in precision and recall. In summary, the improved method proposed in this paper shows strong advantages in various aircraft detection tasks, It can effectively cope with the detection challenges of a wide variety of targets with similar features and complex backgrounds, and has good application prospects.},
booktitle = {Proceedings of the 2025 6th International Conference on Computer Information and Big Data Applications},
pages = {961–967},
numpages = {7},
keywords = {ADown module, YOLOv11, aircraft detection, multi-scale dilated attention, target detection},
location = {
},
series = {CIBDA '25}
}

@article{hybrid_yolo,
author = {Silva, Jamesson and Nogueira, Fabiano and Honório, Douglas and Shiguemori, Elcio and Passaro, Angelo},
year = {2024},
month = {12},
pages = {},
title = {A Hybrid Approach for Target Discrimination in Remote Sensing: Combining YOLO and CNN-Based Classifiers},
volume = {16},
journal = {Journal of Aerospace Technology and Management},
doi = {10.1590/jatm.v16.1353}
}

@article{smote,
   title={SMOTE: Synthetic Minority Over-sampling Technique},
   volume={16},
   ISSN={1076-9757},
   url={http://dx.doi.org/10.1613/jair.953},
   DOI={10.1613/jair.953},
   journal={Journal of Artificial Intelligence Research},
   publisher={AI Access Foundation},
   author={Chawla, N. V. and Bowyer, K. W. and Hall, L. O. and Kegelmeyer, W. P.},
   year={2002},
   month=jun, pages={321–357} }

@INPROCEEDINGS{nms,
  author={Neubeck, A. and Van Gool, L.},
  booktitle={18th International Conference on Pattern Recognition (ICPR'06)}, 
  title={Efficient Non-Maximum Suppression}, 
  year={2006},
  volume={3},
  number={},
  pages={850-855},
  keywords={Computer vision;Data mining;Filters;Application software;Image resolution;Pixel;Image reconstruction;Object recognition;Image texture analysis;Gas insulated transmission lines},
  doi={10.1109/ICPR.2006.479}}

@misc{fpn,
      title={Feature Pyramid Networks for Object Detection}, 
      author={Tsung-Yi Lin and Piotr Dollár and Ross Girshick and Kaiming He and Bharath Hariharan and Serge Belongie},
      year={2017},
      eprint={1612.03144},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/1612.03144}, 
}

@inproceedings{optuna,
    title={Optuna: A Next-generation Hyperparameter Optimization Framework},
    author={Akiba, Takuya and Sano, Shotaro and Yanase, Toshihiko and Ohta, Takeru and Koyama, Masanori},
    booktitle={Proceedings of the 25th {ACM} {SIGKDD} International Conference on Knowledge Discovery and Data Mining},
    year={2019}
}