Abstract
Recent advances in remote sensing technology and high-resolution satellite imagery offer great possibilities for understanding the earth’s surfaces. However, satellite image classification is a challenging problem due to the high variability inherent in satellite data. For this purpose, two learning approaches are proposed and compared for classifying a large-scale dataset including different types of land-use and land-cover surfaces (Eurosat). Traditional (shallow) machine learning models and deep learning models are built by using a set of features extracted from the satellite images for both approaches and using the RGB images for deep models. The best F1-score obtained by the shallow approach was 0.87, while for the deep approach it was 0.91. No significant difference was found in these results; however, significant improvements can be made by exploring the deep approach in greater depth.
The first author was supported by the Mexican National Council for Science and Technology (CONACYT), under the grant number 28602.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bagri, N., Johari, P.K.: A comparative study on feature extraction using texture and shape for content based image retrieval. Int. J. Adv. Sci. Technol. 80(4), 41–52 (2015)
Basu, S., Ganguly, S., Mukhopadhyay, S., DiBiano, R., Karki, M., Nemani, R.: DeepSat: a learning framework for satellite imagery. In: International Conference on Advances in Geographic Information Systems, pp. 1–10 (2015)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Coelho, L.P.: Mahotas: open source software for scriptable computer vision. J. Open Res. Softw. (2012)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Helber, P., Bischke, B., Dengel, A., Borth, D.: EuroSat: a novel dataset and deep learning benchmark for land use and land cover classification. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 12(7), 2217–2226 (2019)
Kohavi, R., John, G.H., et al.: Wrappers for feature subset selection. Artif. Intell. 97(1–2), 273–324 (1997)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)
Lai, Z., Deng, H.: Medical image classification based on deep features extracted by deep model and statistic feature fusion with multilayer perceptron. Comput. Intell. Neurosci. 2018 (2018)
Larochelle, H., Bengio, Y., Louradour, J., Lamblin, P.: Exploring strategies for training deep neural networks. J. Mach. Learn. Res. 10(1) (2009)
Ma, L., Li, M., Ma, X., Cheng, L., Du, P., Liu, Y.: A review of supervised object-based land-cover image classification. ISPRS J. Photogramm. Remote. Sens. 130, 277–293 (2017)
Medhi, S., Ahmed, C., Gayan, R.: A study on feature extraction techniques in image processing. Int. J. Comput. Sci. Eng. 4(7), 89–93 (2016)
Millard, K., Richardson, M.: On the importance of training data sample selection in random forest image classification: a case study in peatland ecosystem mapping. Remote Sens. 7(7), 8489–8515 (2015)
Moya, L., Marval Perez, L.R., Mas, E., Adriano, B., Koshimura, S., Yamazaki, F.: Novel unsupervised classification of collapsed buildings using satellite imagery, hazard scenarios and fragility functions. Remote Sens. 10(2), 296 (2018)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: International Conference on Machine Learning, pp. 807–814 (2010)
Quattoni, A., Collins, M., Darrell, T.: Transfer learning for image classification with sparse prototype representations. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2008)
Verpoorter, C., Kutser, T., Seekell, D.A., Tranvik, L.J.: A global inventory of lakes based on high-resolution satellite imagery. Geophys. Res. Lett. 41(18), 6396–6402 (2014)
Yao, X., Han, J., Cheng, G., Qian, X., Guo, L.: Semantic annotation of high-resolution satellite images via weakly supervised learning. IEEE Trans. Geosci. Remote Sens. 54(6), 3660–3671 (2016)
Zhang, P., Ke, Y., Zhang, Z., Wang, M., Li, P., Zhang, S.: Urban land use and land cover classification using novel deep learning models based on high spatial resolution satellite imagery. Sensors 18(11), 3717 (2018)
Zhou, W., Gao, S., Zhang, L., Lou, X.: Histogram of oriented gradients feature extraction from raw Bayer pattern images. IEEE Trans. Circuits Syst. II Express Briefs 67(5), 946–950 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Sainos-Vizuett, M., Lopez-Nava, I.H. (2021). Satellite Imagery Classification Using Shallow and Deep Learning Approaches. In: Roman-Rangel, E., Kuri-Morales, Á.F., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2021. Lecture Notes in Computer Science(), vol 12725. Springer, Cham. https://doi.org/10.1007/978-3-030-77004-4_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-77004-4_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-77003-7
Online ISBN: 978-3-030-77004-4
eBook Packages: Computer ScienceComputer Science (R0)
