Top

Published in:

2021 | OriginalPaper | Chapter

Incorporating Domain Knowledge in Machine Learning for Satellite Image Processing

Authors : Ambily Pankajakshan, Malay Kumar Nema, Rituraj Kumar

Published in: Advanced Computing

Publisher: Springer Singapore

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

This paper highlights the need for incorporation of domain knowledge in the context of satellite image processing. We take an application area of satellite image processing and make our assertion for incorporation of human domain knowledge. Traditionally, a machine learning based approach do not take general human intelligence into account for training and classification. We suggest to apply general human intelligence through suitable domain knowledge filters on the outcome of a deep classifier network. The results of processing become more suitable for human understanding and decision making after they pass through the domain knowledge-based filters. We devise intuitive filters (not an exhaustive set) and demonstrate the utility of incorporation of domain knowledge with the example of air traffic infrastructure.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Image Forgery Detection & Localization Using Regularized U-Net

next chapter Enabling Oil Production Forecasting Using Machine Learning

Pouyanfar, S., et al.: A survey on deep learning: algorithms, techniques, and applications. ACM Comput. Surv. 5(51), 1–36 (2018)CrossRef

Chen, Y., Xie, Y., Song, L., Chen, F., Tang, T.: A Survey of accelerator architectures for deep neural networks. Engineering 6(3), 264–274 (2020)CrossRef

Capra, M., Bussolino, B., Marchisio, A., Shafique, M., Masera, G., Martina, M.: An updated survey of efficient hardware architectures for accelerating deep convolutional neural networks. Future Internet 12, 113 (2020)CrossRef

Druzhkov, P.N., Kustikova, V.D.: A survey of deep learning methods and software tools for image classification and object detection. Pattern Recogn. Image Anal. 26(1), 9–15 (2016). https://doi.org/10.1134/S1054661816010065CrossRef

Feichtenhofer, C., Pinz, A., Wildes, R.P.: Spatiotemporal multiplier networks for video action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017

Feichtenhofer, C., Pinz, A., Zisserman, A.: Convolutional two-stream network fusion for video action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 2016

Singh, T., Vishwakarma, D.: Human activity recognition in video benchmarks: a survey. In: Rawat, B.S., Trivedi, A., Manhas, S., Karwal, V. (eds.) Advances in Signal Processing and Communication. LNEE, vol. 526, pp. 247–259. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-2553-3_24CrossRef

Zhao, B., Feng, J., Wu, X., Yan, S.: A survey on deep learning-based fine-grained object classification and semantic segmentation. Int. J. Autom. Comput. 14(2), 119–135 (2017). https://doi.org/10.1007/s11633-017-1053-3CrossRef

Xu, J., Xue, K., Zhang, K.: Current status and future trends of clinical diagnoses via image-based deep learning. Theranostics 9(25), 7556–7565 (2019). https://doi.org/10.7150/thno.38065CrossRef

10.

Reichstein, M., Camps-Valls, G., Stevens, B., et al.: Deep learning and process understanding for data-driven earth system science. Nature 566, 195–204 (2019)CrossRef

11.

Pan, Z., Yu, W., Yi, X., Khan, A., Yuan, F., Zheng, Y.: Recent progress on generative adversarial networks (GANs): a survey. IEEE Access 7, 333622–336333 (2019)

12.

Srinivas, S., Sarvadevabhatla, R.K., Mopuri, K.R., Prabhu, N., Kruthiventi, S.S., Babu, R.V.: A taxonomy of deep convolutional neural nets for computer vision. Front. Robot. AI 2, 36 (2016)CrossRef

13.

Montavon, G., Samek, W., Müller, K.R.: Methods for interpreting and understanding deep neural networks. Digital Signal Process. 73, 1–15 (2018)MathSciNetCrossRef

14.

Samek, W., Montavon, G., Vedaldi, A., Hansen, L.K., Müller, K.-R.: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 1st edn. Springer, Cham (2019)

15.

Li, K., Wan, G., Cheng, G., Meng, L., Han, J.: Object detection in optical remote sensing images: A survey and a new benchmark. ISPRS J. Photogram. Remote Sens. 159, 296–307 (2020)CrossRef

16.

Ball, J.E., Anderson, D.T., Chan, C.S.: Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community. J. Appl. Remote Sens. 11(4), 042609 (2017)CrossRef

17.

Daqui, L., Bo, C., Chin, T.-J., Rutten, M.: topological Sweep for Multi-Target Detection of Geostationary Space Objects. IEEE Trans. Signal Process. 68, 5166–5177 (2020)MathSciNetCrossRef

18.

Vota, W.: Oops ! Satellite Imagery cannot predict Human Development Indicators. https://www.ictworks.org/satellite-imagery-human-development/

19.

Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M.: YOLOV4: optimal speed and accuracy of object detection (2020)

Title: Incorporating Domain Knowledge in Machine Learning for Satellite Image Processing
Authors: Ambily Pankajakshan
Malay Kumar Nema
Rituraj Kumar
Publisher: Springer Singapore
Book: Advanced Computing
Print ISBN: 978-981-16-0400-3

Electronic ISBN: 978-981-16-0401-0

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-981-16-0401-0_35

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner