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Aerial images are valuable for observing land, allowing detailed examination of Earth's surface features. As remote sensing (RS) imagery becomes more abundant, there is a growing need to fully utilize these images for smarter Earth observation. Understanding large and complex RS images is crucial. Satellite image scenery categorization, which involves labeling images based on their content, has diverse applications. Deep Learning (DL), using neural networks' powerful attribute learning capabilities, has made significant strides in categorizing satellite imagery scenes. However, recent advances in DL for scenery categorization of RS images are lacking. In our study, we employed three transfer learning (TL) models - VGG16, Densenet201 (D-201), and InceptionV3(IV3) - for classifying aerial images. VGG16 achieved 94% accuracy, while D-201 and IV3 reached 97% accuracy. Combining these models into an ensemble (V3DI ensemble model) improved accuracy to an impressive 99%. This ensemble model combines individual models' classification decisions using majority voting. We demonstrate the efficiency of this approach by showing how ensemble classification accuracy surpasses that of training individual models. Additionally, we preprocess the dataset with a Gabor filter for edge enhancement and denoising to enhance the model's overall performance.
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S. Abraham, A. Aniyan, A. Kembhavi, and N. Philip. Detection of bars in galaxies using a deep convolutional neural network. Monthly Notices of the Royal Astronomical Society 477(1):894–903. 2017. https://doi.org/10.1093/mnras/sty627. (Crossref)
H. S. Alhichri, A. S. Alswayed, Y. Bazi, N. Ammour, and N. A. Alajlan. Classification of remote sensing images using EfficientNet-B3 CNN model with attention. IEEE Access 9:14078-14094. 2021. https://doi.org/10.1109/ACCESS.2021.3051085. (Crossref)
M. Belgiu and L. Drǎguţ. Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing 114:24-31. 04 2016. https://doi.org/10.1016/j.isprsjprs.2016.01.011. (Crossref)
G. Cheng, D. Gao, Y. Liu, and J. Han. Multi-scale and discriminative part detectors based features for multi-label image classification. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18, pp. 649-655. 2018. https://doi.org/10.24963/ijcai.2018/90. (Crossref)
G. Cheng, J. Han, and X. Lu. Remote sensing image scene classification: Benchmark and state of the art. Proceedings of the IEEE 105:1865-1883. 04 2017. https://doi.org/10.1109/JPROC.2017.2675998. (Crossref)
G. Cheng, X. Xie, J. Han, K. Li, and G.-S. Xia. Remote sensing image scene classification meets deep learning: Challenges, methods, benchmarks, and opportunities. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13:3735-3756. 2020. https://doi.org/10.1109/JSTARS.2020.3005403. (Crossref)
G. Cheng, C. Yang, X. Yao, K. Li, and J. Han. When deep learning meets metric learning: Remote sensing image scene classification via learning discriminative CNNs. IEEE Transactions on Geoscience and Remote Sensing 56(5):2811-2821. 2018. https://doi.org/10.1109/TGRS.2017.2783902. (Crossref)
B. Cui, X. Chen, and Y. Lu. Semantic segmentation of remote sensing images using transfer learning and deep convolutional neural network with dense connection. IEEE Access 8:116744-116755. 2020. https://doi.org/10.1109/ACCESS.2020.3003914. (Crossref)
B. Demir and L. Bruzzone. A novel active learning method in relevance feedback for content-based remote sensing image retrieval. IEEE Transactions on Geoscience and Remote Sensing 53(5):2323-2334. 2015. https://doi.org/10.1109/TGRS.2014.2358804. (Crossref)
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, et al. Generative adversarial networks. Communications of the ACM 63(11):139-144. 2020. https://doi.org/10.1145/3422622. (Crossref)
G. Gui, H. Huang, Y. Song, and H. Sari. Deep learning for an effective non-orthogonal multiple access scheme. IEEE Transactions on Vehicular Technology 67(9):8440-8450. 2018. https://doi.org/10.1109/TVT.2018.2848294. (Crossref)
Y. Hua, L. Mou, and X. Zhu. Recurrently exploring class-wise attention in a hybrid convolutional and bidirectional LSTM network for multi-label aerial image classification. ISPRS Journal of Photogrammetry and Remote Sensing 149:188-199. 2019. https://doi.org/10.1016/j.isprsjprs.2019.01.015. (Crossref)
Y. Hua, L. Mou, and X. X. Zhu. Relation network for multilabel aerial image classification. IEEE Transactions on Geoscience and Remote Sensing 58(7):4558-4572. 2020. https://doi.org/10.1109/TGRS.2019.2963364. (Crossref)
H. Huang, Y. Peng, J. Yang, W. Xia, and G. Gui. Fast beamforming design via deep learning. IEEE Transactions on Vehicular Technology 69(1):1065-1069. 2019. https://doi.org/10.1109/TVT.2019.2949122. (Crossref)
H. Huang, J. Yang, Y. Song, H. Huang, and G. Gui. Deep learning for super-resolution channel estimation and doa estimation based massive mimo system. IEEE Transactions on Vehicular Technology 67(9):8549-8560. 2018. https://doi.org/10.1109/TVT.2018.2851783. (Crossref)
H. Jiang, D. Zhao, Y. Cai, and S. An. A method for application of classification tree models to map aquatic vegetation using remotely sensed images from different sensors and dates. Sensors 12(9):12437-12454. 2012. https://doi.org/10.3390/s120912437. (Crossref)
Z. Karim and T. van Zyl. Deep learning and transfer learning applied to sentinel-1 dinsar and sentinel-2 optical satellite imagery for change detection. In: 2020 International SAUPEC/RobMech/PRASA Conference, pp. 1-7. 2020. https://doi.org/10.1109/SAUPEC/RobMech/PRASA48453.2020.9041139. (Crossref)
N. Kato, Z. Fadlullah, B. Mao, F. Tang, O. Akashi, et al. The deep learning vision for heterogeneous network traffic control: Proposal, challenges, and future perspective. IEEE Wireless Communications 24(3):146-153. 2017. https://doi.org/10.1109/MWC.2016.1600317WC. (Crossref)
N. Khan, U. Chaudhuri, B. Banerjee, and S. Chaudhuri. Graph convolutional network for multi-label vhr remote sensing scene recognition. Neurocomputing 357:36-46. 2019. https://doi.org/10.1016/j.neucom.2019.05.024. (Crossref)
W. Li, H. Liu, Y. Wang, L. Zhuangzhuang, Y. Jia, et al. Deep learning-based classification methods for remote sensing images in urban built-up areas. IEEE Access 7:36274-36284. 2019. https://doi.org/10.1109/ACCESS.2019.2903127. (Crossref)
W. Li, Z. Wang, Y. Wang, J. Wu, J. Wang, et al. Classification of high-spatial-resolution remote sensing scenes method using transfer learning and deep convolutional neural network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13:1986-1995. 2020. https://doi.org/10.1109/JSTARS.2020.2988477. (Crossref)
H. Liu, X. Huang, F. Han, J. Cui, B. Spencer, et al. Hybrid polarimetric GPR calibration and elongated object orientation estimation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13:1986-1995. 2019. https://doi.org/10.1109/JSTARS.2019.2912339. (Crossref)
H. Liu, H. Xia, M. Zhuang, Z. Long, C. Liu, et al. Reverse time migration of acoustic waves for imaging based defects detection for concrete and CFST structures. Mechanical Systems and Signal Processing 117:210-220. 2019. https://doi.org/10.1016/j.ymssp.2018.07.011. (Crossref)
B. Mao, F. Tang, Z. Fadlullah, N. Kato, O. Akashi, et al. A novel non-supervised deep-learning-based network traffic control method for software defined wireless networks. IEEE Wireless Communications 25(4):74-81. 2018. https://doi.org/10.1109/MWC.2018.1700417. (Crossref)
M. Mesvari and R. Shah-Hosseini. Segmentation of electrical substations using deep convolutional neural network. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W1-2022:495-500. 2023. https://doi.org/10.5194/isprs-annals-X-4-W1-2022-495-2023. (Crossref)
G. Mountrakis, J. Im, and C. Ogole. Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing 66(3):247-259. 2011. https://doi.org/10.1016/j.isprsjprs.2010.11.001. (Crossref)
F. A. Oliehoek, R. Savani, J. Gallego-Posada, E. Van der Pol, E. D. De Jong, et al. GANGs: Generative adversarial network games. arXiv, arXiv:1712.00679. 2017. https://doi.org/10.48550/arXiv.1712.00679.
B. Petrovska, E. Zdravevski, P. Lameski, R. Corizzo, I. Štajduhar, et al. Deep learning for feature extraction in remote sensing: A case-study of aerial scene classification. Sensors 20(14):3906. 2020. https://doi.org/10.3390/s20143906. (Crossref)
D. F. Santos, R. G. Pires, L. A. Passos, and J. P. Papa. DDIPNet and DDIPNet+: Discriminant deep image prior networks for remote sensing image classification. In: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, pp. 2843-2846. 2021. https://doi.org/10.1109/IGARSS47720.2021.9554277. (Crossref)
Z. Shao, J. Cai, P. Fu, L. Hu, and T. Liu. Deep learning-based fusion of Landsat-8 and Sentinel-2 images for a harmonized surface reflectance product. Remote Sensing of Environment 235:111425. 2019. https://doi.org/10.1016/j.rse.2019.111425. (Crossref)
Z. Shao, W. Zhou, X. Deng, M. Zhang, and Q. Cheng. Multilabel remote sensing image retrieval based on fully convolutional network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13:318-328. 2020. https://doi.org/10.1109/JSTARS.2019.2961634. (Crossref)
R. Stivaktakis, G. Tsagkatakis, and P. Tsakalides. Deep learning for multilabel land cover scene categorization using data augmentation. IEEE Geoscience and Remote Sensing Letters 16(7):1031-1035. 2019. https://doi.org/10.1109/LGRS.2019.2893306. (Crossref)
F. Tang, Z. Fadlullah, B. Mao, and N. Kato. An intelligent traffic load prediction based adaptive channel assignment algorithm in SDN-IoT: A deep learning approach. IEEE Internet of Things Journal 5(6):5141-5154. 2018. https://doi.org/10.1109/JIOT.2018.2838574. (Crossref)
R. Verma and J. Ali. A comparative study of various types of image noise and efficient noise removal techniques. International Journal of Advanced Research in Computer Science and Software Engineering 3:617-622. 2013.
Y. Wang, M. Liu, J. Yang, and G. Gui. Data-driven deep learning for automatic modulation recognition in cognitive radios. IEEE Transactions on Vehicular Technology 68(4):4074-4077. 2019. https://doi.org/10.1109/TVT.2019.2900460. (Crossref)
B. Xia, F. Kong, J. Zhou, X. Wu, and Q. Xie. Land resource use classification using deep learning in ecological remote sensing images. Computational Intelligence and Neuroscience 2022:7179477. 2022. https://doi.org/10.1155/2022/7179477. (Crossref)
Y. Zhong, X. Han, and L. Zhang. Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing 138:281-294. 2018. https://doi.org/10.1016/j.isprsjprs.2018.02.014. (Crossref)
X. X. Zhu, D. Tuia, L. Mou, G.-S. Xia, L. Zhang, et al. Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine 5(4):8-36. 2017. https://doi.org/10.1109/MGRS.2017.2762307. (Crossref)
Q. Zou, L. Ni, T. Zhang, and Q. Wang. Deep learning based feature selection for remote sensing scene classification. IEEE Geoscience and Remote Sensing Letters 12(11):2321-2325. 2015. https://doi.org/10.1109/LGRS.2015.2475299. (Crossref)