Skip to main content
SpringerLink
Log in

Quantum neural network-based multilabel image classification in high-resolution unmanned aerial vehicle imagery

  • Focus
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Latest advancements in real-time remote sensing sensors and platform lead to the development of unmanned aerial vehicles (UAV) which enable the accessibility of high-resolution imaging data. Since image classification appears like a basic interconnection among aerial images and the corresponding applications, it intends to categorize images into semantic classes. Several earlier works have concentrated on the classification of an image into a single semantic label, whereas in the real world, an aerial image is commonly interrelated to many class labels, for instance, multiple object-level labels. Moreover, a broad image of present objects in provided high-resolution aerial image offers an extensive interpretation of the investigated area. To attain this, this paper presents a new quantum neural network based multilabeled aerial image classification (QNN-MLAIC) model. The proposed QNN-MLAIC model involves different processes, namely image acquisition, preprocessing, object detection, feature extraction, and classification. Initially, the aerial images are acquired by wireless UAVs and are preprocessed. Then, the Faster RCNN technique with Inception with Residual Network-v2 model as the baseline model is applied as an object detector, which detects the existence of multiple objects in the aerial image and generates a helpful set of feature vectors. Finally, QNN is employed as classifier, which categorizes the aerial images into multiple class labels. In order to tune the parameters involved in QNN model, the beetle antenna search algorithm is employed. Detailed performance analysis of the proposed QNN-MLAIC model takes place using the UC Merced dataset (UCM) aerial dataset, and the outcomes are investigated under many dimensions. The experimental outcome ensured the goodness of the presented QNN-MLAIC method on the applied UCM aerial dataset over the compared methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

References

  • Alshehri A, Bazi Y, Ammour N, Almubarak H, Alajlan N (2019) Deep attention neural network for multi-label classification in unmanned aerial vehicle imagery. IEEE Access 7:119873–119880

    Article  Google Scholar 

  • Baranwal E, Raghvendra S, Tiwari PS, Pande H (2021) Health monitoring and assessment of the cultural monument through unmanned aerial vehicle (UAV) image processing. In: Advances in systems engineering. Springer, Singapore, pp 145–160

  • Bashmal L, Bazi Y, Al Rahhal MM, Alhichri H, Al Ajlan N (2021) UAV image multi-labeling with data-efficient transformers. Appl Sci 11(9):3974

    Article  Google Scholar 

  • Blaschke T, Hay GJ, Kelly M, Lang S, Hofmann P, Addink E, Feitosa RQ, van der Meer F, van der Werff H, van Coillie F et al (2014) Geographic object-based image analysis—towards a new paradigm. ISPRS J Photogramm Remote Sens 87:180–191

    Article  Google Scholar 

  • Boonpook W, Tan Y, Ye Y, Torteeka P, Torsri K, Dong S (2018) A deep learning approach on building detection from unmanned aerial vehicle-based images in riverbank monitoring. Sensors 18(11):3921

    Article  Google Scholar 

  • Borlea ID, Precup RE, Borlea AB, Iercan D (2021) A unified form of fuzzy C-means and K-means algorithms and its partitional implementation. Knowl Based Syst 214:106731

    Article  Google Scholar 

  • Cong M, Wang Z, Tao Y, Xi J, Ren C, Xu M (2019) Unsupervised self-adaptive deep learning classification network based on the optic nerve microsaccade mechanism for unmanned aerial vehicle remote sensing image classification. Geocarto Int 89:1–20

    Google Scholar 

  • Cong M, Xi J, Han L, Gu J, Yang L, Tao Y, Xu M (2020) Multi-resolution classification network for high-resolution UAV remote sensing images. Geocarto Int 56:1–25

    Google Scholar 

  • Guan W, Zhou H, Su Z, Zhang X, Zhao C (2019) Ship steering control based on quantum neural network. Complexity 2019:1–10

    Google Scholar 

  • Helge A, Eija H, Arko L, Pablo JZT (2018) Quantitative remote sensing at ultra-high resolution with UAV spectroscopy: a review of sensor technology, measurement procedures, and data correction workflows. Remote Sens 10(7):1091

    Article  Google Scholar 

  • http://weegee.vision.ucmerced.edu/datasets/landuse.html

  • https://captain-whu.github.io/AID/

  • Hua Y, Mou L, Zhu XX (2019) Recurrently exploring class-wise attention in a hybrid convolutional and bidirectional LSTM network for multi-label aerial image classification. ISPRS J Photogramm Remote Sens 149:188–199

    Article  Google Scholar 

  • Huang Q, Xie L, Yin G, Ran M, Liu X, Zheng J (2020) Acoustic signal analysis for detecting defects inside an arc magnet using a combination of variational mode decomposition and beetle antennae search. ISA Trans 102:347–364

    Article  Google Scholar 

  • Jiao W, Wang L, McCabe MF (2021) Multi-sensor remote sensing for drought characterization: current status, opportunities and a roadmap for the future. Remote Sens Environ 256:112313

    Article  Google Scholar 

  • Kamble RM, Chan GC, Perdomo O, Kokare M, Gonzalez FA, Müller H, Mériaudeau F (2018) Automated diabetic macular edema (DME) analysis using fine tuning with inception-resnet-v2 on OCT images. In: 2018 IEEE-EMBS conference on biomedical engineering and sciences (IECBES). IEEE, pp 442–446

  • Lei X, Sui Z (2019) Intelligent fault detection of high voltage line based on the Faster R-CNN. Measurement 138:379–385

    Article  Google Scholar 

  • Li Y, Chen R, Zhang Y, Zhang M, Chen L (2020) Multi-label remote sensing image scene classification by combining a convolutional neural network and a graph neural network. Remote Sens 12(23):4003

    Article  Google Scholar 

  • Lin D, Lin J, Zhao L, Wang ZJ, Chen Z (2021) Multilabel aerial image classification with a concept attention graph neural network. IEEE Trans Geosci Remote Sens 23:1–12

    Google Scholar 

  • Moser G, Serpico SB, Benediktsson JA (2013) Land-cover mapping by Markov modeling of spatial-contextual information in very-high-resolution remote sensing images. Proc IEEE 101:631–651

    Article  Google Scholar 

  • Munawar HS, Ullah F, Qayyum S, Heravi A (2021) Application of deep learning on UAV-based aerial images for flood detection. Smart Cities 4(3):1220–1242

    Article  Google Scholar 

  • Najafi P, Feizizadeh B, Navid H (2021) A comparative approach of fuzzy object based image analysis and machine learning techniques which are applied to crop residue cover mapping by using Sentinel-2 satellite and UAV imagery. Remote Sens 13(5):937

    Article  Google Scholar 

  • Pustokhina IV, Pustokhin DA, Kumar Pareek P, Gupta D, Khanna A, Shankar K (2021) Energy-efficient cluster-based unmanned aerial vehicle networks with deep learning-based scene classification model. Int J Commun Syst 34(8):e4786

    Article  Google Scholar 

  • Saeed S, Latif MA, Rajput MA (2021) Fuzzy-based multi-crop classification using high resolution UAV imagery. Quaid-E-Awam Univ Res J Eng Sci Technol Nawabshah 19(1):1–8

    Google Scholar 

  • Sobrino JA, Frate FD, Drusch M, Jimenez-Munoz JC, Manunta P, Regan A (2016) Review of thermal infrared applications and requirements for future high-resolution sensors. IEEE Trans Geosci Remote Sens 54(5):2963–2972

    Article  Google Scholar 

  • Sumbul G, Demİr B (2020) A deep multi-attention driven approach for multi-label remote sensing image classification. IEEE Access 8:95934–95946

    Article  Google Scholar 

  • Szegedy C, Ioffe S, Vanhoucke V, Alemi AA (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: AAAI

  • Tan Q, Liu Y, Chen X, Yu G (2017) Multi-label classification based on low rank representation for image annotation. Remote Sens 9(2):109

    Article  Google Scholar 

  • Xie H, Chen Y, Ghamisi P (2021) Remote sensing image scene classification via label augmentation and intra-class constraint. Remote Sens 13(13):2566

    Article  Google Scholar 

  • Zeggada A, Benbraika S, Melgani F, Mokhtari Z (2018) Multilabel conditional random field classification for UAV images. IEEE Geosci Remote Sens Lett 15(3):399–403

    Article  Google Scholar 

  • Zhang X, Han L, Dong Y, Shi Y, Huang W, Han L, González-Moreno P, Ma H, Ye H, Sobeih T (2019) A deep learning-based approach for automated yellow rust disease detection from high-resolution hyperspectral UAV images. Remote Sens 11(13):1554

    Article  Google Scholar 

  • Zhou Y, Zhang R, Wang S, Wang F (2018) Feature selection method based on high-resolution remote sensing images and the effect of sensitive features on classification accuracy. Sensors 18(7):2013

    Article  Google Scholar 

Download references

Acknowledgements

Taif University Researchers Supporting Project number (TURSP-2020/154), Taif University, Taif, Saudi Arabia.

Author information

Authors and Affiliations

  1. Department of Mathematics and Statistics, College of Science, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia

    Sayed Abdel-Khalek

  2. Mathematical Sciences Department, College of Science, Princess Nourah Bint Abdulrahman University, Riyadh, 11564, Saudi Arabia

    Mariam Algarni

  3. Department of Mathematics, Faculty of Science, New Valley University, El-Kharga, 72511, Egypt

    Romany F. Mansour

  4. Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, Delhi, India

    Deepak Gupta

  5. Department of Computer Science and Information Technology, Kalasalingam Academy of Research and Education, Krishnankoil, India

    M. Ilayaraja

Authors
  1. Sayed Abdel-Khalek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mariam Algarni
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Romany F. Mansour
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Deepak Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. M. Ilayaraja
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Romany F. Mansour.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest. The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

Additional information

Communicated by Oscar Castillo.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abdel-Khalek, S., Algarni, M., Mansour, R.F. et al. Quantum neural network-based multilabel image classification in high-resolution unmanned aerial vehicle imagery. Soft Comput 27, 13027–13038 (2023). https://doi.org/10.1007/s00500-021-06460-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-021-06460-3

Keywords

Search

Navigation