Satellite Imagery Classification with Deep Learning : A Survey

Authors

  • Niharika Goswami  U.G. Scholar, Sigma Institute of Engineering, Vadodara, Gujarat, India
  • Keyurkumar Kathiriya  U.G. Scholar, Sigma Institute of Engineering, Vadodara, Gujarat, India
  • Santosh Yadav  U.G. Scholar, Sigma Institute of Engineering, Vadodara, Gujarat, India
  • Janki Bhatt  U.G. Scholar, Sigma Institute of Engineering, Vadodara, Gujarat, India
  • Dr. Sheshang Degadwala  Associate Professor, Sigma Institute of Engineering, Vadodara, Gujarat, India

DOI:

https://doi.org/10.32628/CSEIT2065124

Keywords:

SVM, KNN, CNN, RNN, Lung Cancer, Stages, CT Image, Nodules, Deep Learning

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 satellite images. In the past decades satellite imagery has been used successfully for weather forecasting, geographical and geological applications. Low resolution satellite images are sufficient for these sorts of applications. But the technological developments in the field of satellite imaging provide high resolution sensors which expands its field of application. Thus, the High-Resolution Satellite Imagery (HRSI) proved to be a suitable alternative to aerial photogrammetric data to provide a new data source for object detection. Since the traffic rates in developing countries are enormously increasing, vehicle detection from satellite data will be a better choice for automating such systems. In this research, a different technique for vehicle detection from the images obtained from high resolution sensors is reviewed. This review presents the recent progress in the field of object detection from satellite imagery using deep learning.

References

  1. N. L. Tun, A. Gavrilov, and N. M. Tun, “Multi-classification of satellite imagery using fully convolutional neural network,” Proc. - 2020 Int. Conf. Ind. Eng. Appl. Manuf. ICIEAM 2020, pp. 7–11, 2020, doi: 10.1109/ICIEAM48468.2020.9111928.
  2. A. Van Etten, “Satellite imagery multiscale rapid detection with windowed networks,” Proc. - 2019 IEEE Winter Conf. Appl. Comput. Vision, WACV 2019, pp. 735–743, 2019,
  3. A. Van Etten, “You Only Look Twice: Rapid Multi-Scale Object Detection in Satellite Imagery,” 2018.
  4. Y. Koga, H. Miyazaki, and R. Shibasaki, “Correction: A Method for Vehicle Detection in High-Resolution Satellite Images That Uses a Region-Based Object Detector and Unsupervised Domain Adaptation. [Remote Sensing 2020, 12, 575] doi: 10.3390/rs12030575,” Remote Sens., vol. 12, no. 7,
  5. R. F. Berriel, A. T. Lopes, A. F. De Souza, and T. Oliveira-Santos, “Deep Learning-Based Large-Scale Automatic Satellite Crosswalk Classification,” IEEE Geosci. Remote Sens. Lett., vol. 14, no. 9, pp. 1513–1517, 2017, doi: 10.1109/LGRS.2017.2719863.
  6. T. Ophoff, S. Puttemans, V. Kalogirou, J. P. Robin, and T. Goedemé, “Vehicle and vessel detection on satellite imagery: A comparative study on single-shot detectors,” Remote Sens., vol. 12, no. 7, pp. 1–21, 2020, doi: 10.3390/rs12071217.
  7. J. Yuan, “Automatic Building Extraction in Aerial Scenes Using Convolutional Networks,” 2016, [Online]. Available: http://arxiv.org/abs/1602.06564.
  8. M. Pritt and G. Chern, “Satellite image classification with deep learning,” Proc. - Appl. Imag. Pattern Recognit. Work., vol. 2017-October, pp. 1–7, 2018, doi: 10.1109/AIPR.2017.8457969.
  9. “Three Applications Of Deep Learning Algorithms For Object Detection Milena Napiorkowska ( 1 ), David Petit ( 1 ), Paula Martí ( 2 ) ( 1 ) Deimos Space UK Ltd ., ( 2 ) Deimos Engenharia,” no. 1, pp. 4839–4842, 2018.
  10.  V. Iglovikov, S. Mushinskiy, and V. Osin, “Satellite Imagery Feature Detection using Deep Convolutional Neural Network: A Kaggle Competition,” 2017, [Online]. Available: http://arxiv.org/abs/1706.06169.
  11. E. Kalinicheva, J. Sublime, and M. Trocan, “Object-Based Change Detection in Satellite Images Combined with Neural Network Autoencoder Feature Extraction,” 2019 9th Int. Conf. Image Process. Theory, Tools Appl. IPTA 2019, pp. 1–6, 2019, doi: 10.1109/IPTA.2019.8936085.
  12. C. Wang, Q. Jiang, M. Cheng, J. Li, and L. Cao, “Deep Neural Networks-Based Vehicle Detection In Satellite Images Fujian Key Laboratory of Sensing and Computing for Smart City School of Information Science and Engineering, Xiamen University Xiamen, China,” IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work., 2016.
  13.  T. Ishii et al., “Detection by classification of buildings in multispectral satellite imagery,” Proc. - Int. Conf. Pattern Recognit., vol. 0, pp. 3344–3349, 2016, doi: 10.1109/ICPR.2016.7900150.
  14. A. Mansour, A. Hassan, W. M. Hussein, and E. Said, “Automated vehicle detection in satellite images using deep learning,” IOP Conf. Ser. Mater. Sci. Eng., vol. 610, no. 1, 2019, doi: 10.1088/1757-899X/610/1/012027.
  15. G. Cheng, J. Han, and X. Lu, “Remote Sensing Image Scene Classification: Benchmark and State of the Art,” Proc. IEEE, vol. 105, no. 10, pp. 1865–1883, 2017, doi: 10.1109/JPROC.2017.2675998.
  16. Y. H. Robinson, S. Vimal, M. Khari, F. C. L. Hernández, and R. G. Crespo, “Tree-based convolutional neural networks for object classification in segmented satellite images,” Int. J. High Perform. Comput. Appl., 2020, doi: 10.1177/1094342020945026.
  17. N. Imamoglu, P. Martínez-Gómez, R. Hamaguchi, K. Sakurada, and R. Nakamura, “Exploring recurrent and feedback CNNs for multi-spectral satellite image classification,” Procedia Comput. Sci., vol. 140, pp. 162–169, 2018, doi: 10.1016/j.procs.2018.10.325.
  18. A. Hosny and A. Parziale, “A Study on Deep Learning,” vol. 9, no. 4, pp. 21482–21483, 2019.
  19. D. Dai and W. Yang, “Satellite image classification via two-layer sparse coding with biased image representation,” IEEE Geosci. Remote Sens. Lett., vol. 8, no. 1, pp. 173–176, 2011, doi: 10.1109/LGRS.2010.2055033.
  20. J. Han, D. Zhang, G. Cheng, L. Guo, and J. Ren, “Han_etal_IEEE_TGRS_2015_Object_detection_in_optical_remote_sensing_images_based_weakly.pdf,” pp. 1–26.
  21. O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 9351, pp. 234–241, 2015, doi: 10.1007/978-3-319-24574-4_28.
  22. P. Helber, B. Bischke, A. Dengel, and D. Borth, “Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 12, no. 7, pp. 2217–2226, 2019, doi: 10.1109/JSTARS.2019.2918242.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2020-11-30

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Volume 6, Issue 6, November-December-2020

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Research Articles

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How to Cite

[1]
Niharika Goswami, Keyurkumar Kathiriya, Santosh Yadav, Janki Bhatt, Dr. Sheshang Degadwala, " Satellite Imagery Classification with Deep Learning : A Survey " International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 6, pp.36-46, November-December-2020. Available at doi : https://doi.org/10.32628/CSEIT2065124