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.
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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
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
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
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
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
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
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
Guan W, Zhou H, Su Z, Zhang X, Zhao C (2019) Ship steering control based on quantum neural network. Complexity 2019:1–10
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
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
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
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
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
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
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
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
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
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
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
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
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
Sumbul G, Demİr B (2020) A deep multi-attention driven approach for multi-label remote sensing image classification. IEEE Access 8:95934–95946
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
Xie H, Chen Y, Ghamisi P (2021) Remote sensing image scene classification via label augmentation and intra-class constraint. Remote Sens 13(13):2566
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
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
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
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Taif University Researchers Supporting Project number (TURSP-2020/154), Taif University, Taif, Saudi Arabia.
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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
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DOI: https://doi.org/10.1007/s00500-021-06460-3