Abstract
Machine learning applications in remote sensing often require a labour-intensive feature engineering step, if only a small number of samples is available and transfer learning is not applicable. Here, we are introducing the concept of Spatial Variation Sequences, which allows to apply methodologies from automated time-series feature engineering to remote sensing applications of static images. The presented example application detects swimming pools from four-channel satellite images with an \(F_{1}\)-score of 0.95, by generating spatial variation sequences from a modified swimming pool index. The automated feature engineering approach reduced the dimensionality of the classification problem by 99.7%. A more traditional approach using transfer learning on pre-trained Convolutional Neural Networks (CNN) was evaluated in parallel for comparison. The CNN approach boasted a higher performance of \(F_{1}\)-score of 0.98 but required the use of pre-trained weights. The comparable performance of the FE and CNN approach demonstrates that time-series feature extraction is a valuable alternative to traditional remote sensing methods in the presence of data scarcity or the need of significant dimensionality reduction.
J. Jeune, N. Pechan, Sh.-K. Reitsma—Work was performed while the author was with the Department.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
https://tsfresh.readthedocs.io/en/v0.11.1/text/list_of_features.html has the same features as tsfresh v0.11.2, which is used for this study.
References
Christ, M., Braun, N., Neuffer, J., Kempa-Liehr, A.W.: Time series FeatuRe extraction on basis of scalable hypothesis tests (tsfresh - a Python package). Neurocomputing 307, 72–77 (2018). https://doi.org/10.1016/j.neucom.2018.03.067
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 (2009). https://doi.org/10.1109/CVPR.2009.5206848
Dietterichl, T.G.: Ensemble learning. In: Arbib, M. (ed.) The Handbook of Brain Theory and Neural Networks, pp. 405–408. MIT Press (2002)
Dimitrovski, I., Kitanovski, I., Kocev, D., Simidjievski, N.: Current trends in deep learning for earth observation: an open-source benchmark arena for image classification. ISPRS J. Photogramm. Remote. Sens. 197, 18–35 (2023). https://doi.org/10.1016/j.isprsjprs.2023.01.014
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90
Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Alakhutdinov, R.: Improving neural networks by preventing co-adaptation of feature detectors. cs.NE 1207.0580v1, arXiv https://arxiv.org/abs/1207.0580v1 (2012)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105. Curran Associates, Inc. (2012)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017). https://doi.org/10.1145/3065386
Li, W., et al.: Classification of high-spatial-resolution remote sensing scenes method using transfer learning and deep convolutional neural network. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 13, 1986–1995 (2020). https://doi.org/10.1109/JSTARS.2020.2988477
Lu, D., Weng, Q.: A survey of image classification methods and techniques for improving classification performance. Int. J. Remote Sens. 28(5), 823–870 (2007). https://doi.org/10.1080/01431160600746456
Ministry of Business Innovation and Employment, N.Z.: Building (Pools) Amendment Act 2016 (2016). https://www.legislation.govt.nz/act/public/2016/0071/latest/DLM6581358.html
Morrison, L., Chalmers, D.J., Langley, J.D., Alsop, J.C., McBean, C.: Achieving compliance with pool fencing legislation in New Zealand: a survey of regulatory authorities. Inj. Prev. 5(2), 114–118 (1999). https://doi.org/10.1136/ip.5.2.114
Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Learning and transferring mid-level image representations using convolutional neural networks. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1717–1724 (2014). https://doi.org/10.1109/CVPR.2014.222
Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. cs.LG 1912.01703, arXiv https://arxiv.org/abs/1912.01703 (2019)
Rodríguez-Cuenca, B., Alonso, M.: Semi-automatic detection of swimming pools from aerial high-resolution images and LIDAR data. Remote Sens. 6(4), 2628–2646 (2014). https://doi.org/10.3390/rs6042628
Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14–16, 2014, Workshop Track Proceedings, pp. 1–8 (2014). http://arxiv.org/abs/1312.6034
Sobel, I.: An isotropic 3\(\times \) 3 image gradient operator. In: Machine Vision for Three-Dimensional Scenes, pp. 376–379 (1990)
Acknowledgements
This research is indebted to the Gisborne District Council. Without their willingness to provide aerial photography and land parcel data, this research would not have been possible.
The Machine Learning team at Xero is also acknowledged for providing a wealth of knowledge and advice around the training and interpretation of convolutional neural networks.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Jeune, H., Pechan, N., Reitsma, SK., Kempa-Liehr, A.W. (2024). Spatial Variation Sequences for Remote Sensing Applications with Small Sample Sizes. In: Yan, W.Q., Nguyen, M., Nand, P., Li, X. (eds) Image and Video Technology. PSIVT 2023. Lecture Notes in Computer Science, vol 14403. Springer, Singapore. https://doi.org/10.1007/978-981-97-0376-0_12
Download citation
DOI: https://doi.org/10.1007/978-981-97-0376-0_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-0375-3
Online ISBN: 978-981-97-0376-0
eBook Packages: Computer ScienceComputer Science (R0)