Abstract
Cloud detection of satellite images is a challenging task. Extracting discriminative image features is one of the crucial steps for accurate cloud detection. In this chapter, we introduce a texture-based image feature that combines the merits of grey-level co-occurrence matrix (GLCM) features and rotation invariant uniform local binary pattern (RIULBP). The cloud detection method based on proposed feature consists of three steps: (1) Enhancing the image and dividing it into non-overlap patches; (2) Calculating GLCM features and RIULBP independently on patches and determining their optimal key parameters based on cloud detection performance; (3) Combining optimal GLCM features and RIULBP and feeding them into SVM classifier to identify patches with cloud. The proposed detection method is quantitatively compared to methods that only use GLCM features and RIULBP. The overall detection accuracy shows that our proposed method outperforms the GLCM and RIULBP method on real images. The proposed cloud detection method facilitates cloud segmentation and classification tasks, which can aid to better analysis of satellite image.
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
Tian, B., Shaikh, A., Azimi-Sajadi, M.R., Vonder Haar, T.H., Reinke, C.L.: A study of cloud classification with neural networks using spectral and textural features. IEEE Trans. Neural Netw. 10, 138–152 (1999)
Liu, L., Sun, X., Chen, F., et al.: Cloud classification based on structure features of infrared images. J. Atmos. Ocean. Technol. 28, 410–417 (2011)
Isosalo, A., Turtinen, M., Pietikainen, M.: Cloud characterization using local texture information. In: Proceedings of the Finnish Signal Processing Symposium, Oulu, Finland, pp. 1–6 (2007)
Calbo, J., Sabburg, J.: Feature extraction from whole-sky ground-based images for cloud-type recognition. J. Atmos. Ocean. Technol. 25, 3–12 (2008)
Heinle, A., Macke, A., Srivastav, A.: Automatic cloud classification of whole sky images. Atmos. Meas. Tech. Dis. 3, 557–567 (2010)
Zhou, W., Cao, Z., Xiao, Y.: Cloud classification of ground-based images using texture-structure features. J. Atmos. Ocean. Technol. 31, 79–92 (2014)
Singh, M., Glennen, M.: Automated ground-based cloud recognition. Formal Pattern Anal. Appl. 8, 258–271 (2005)
Christodoulou, C.I., Pattichis, C.S., Pantziaris, M., et al.: Texture-based classification of atherosclerotic carotid plaques. IEEE Tans. Med. Imaging. 22, 902–912 (2003)
Xie, M.H., Li, R.Y., Tian, Y.Q., et al.: The removing clouds method based on large remote sensing image. J. Beijing Normal Univ. 42, 42–47 (2006)
Azimi-Sadjadi, M.R., Zekavat, S.A.: Cloud classification using support vector machine. vol. 31, pp. 669–671 (2000)
Chen, Y., Zhang, C.: Multi-features cloud classification based on SVM and fractal dimension. Int. J. Digital Content Technol. Appl. 6, 211–220 (2013)
Zhou, X., et al.: Salient binary pattern for ground-based cloud classification. Acta. Meteor. Sin. 27, 211–220 (2013)
Zhou, X., Wu, F.: An improved approach to remove cloud and mist from remote sensing images based on the Mallat algorithm. Proc. SPIE Int. Soc. Opt. Eng. (2008)
Ma, J., Wang, C.: Image fusion for automatic detection and removal of clouds and their shadows. Proc. SPIE. 6419, 64191X (2006)
Lee, J., Weger, R.C., Sengupta, S.K., et al.: A neural network approach to cloud classification. IEEE Tans. Geosci. Remote Sens. 28, 846–855 (1990)
Welch, R.M., Sengupta, S.K., Goroch, A.K., et al.: Polar cloud and surface classification using AVHRR imagery: An intercomparison of methods. J. Appl. Meteorol. 31, 405–420 (1992)
Tian, B., Shaikh, M.A., Azimi-Sadjadi, M.R., et al.: A study of cloud classification with neural networks using spectral and textural features. IEEE Trans. Neural Netw. 10, 138–151 (1999)
Buch, K.A., Sun, C.H.: Cloud classification using whole-sky imager data. In: Ninth Symposium on Meteorological Observations & Instrumentation, vol. 16, pp. 353–358 (1995)
Liang, D., Kong, J., Hu, G.S., et al.: The removal of thick cloud and cloud shadow of remote sensing image based on support vector machine. Acta Geodaetica et Cartographica Sinica. 41, 225–232 (2012)
Kong, J., Hu, G.S., Liang, D.: Thin cloud removing approach of remote sensing image based on support vector machine. Comp. Eng. Design. 32, 599–602 (2011)
Gonzalez, R.C., Woods, R.E.: Digital image processing. Prentice Hall, Upper Saddle River (2003)
Soh, L.K., Tsatsoulis, C.: Texture analysis of SAR sea ice imagery using grey level co-occurrence matrices. IEEE Trans. Geosci. Remote Sens. 37, 780–795 (1999)
Al-Janobi, A.: Performance evaluation of cross-diagonal texture matrix method of texture analysis. Pattern Recogn. 34, 171–180 (2001)
Ojala, T., Pietikainen, M.: Multiresolution gray-scale and rotation invariant texture classification with local binary pattern. IEEE Trans. Pattern Anal. Mach. Intell. 24, 971–987 (2002)
Acknowledgement
This work is supported by Hunan Provincial Natural Science Foundation of China (2019JJ50732). We would like to thank all reviewers for their helpful insights and suggestions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Sun, X., Yu, Q., Li, Z. (2020). SVM-Based Cloud Detection Using Combined Texture Features. In: Urbach, H., Yu, Q. (eds) 5th International Symposium of Space Optical Instruments and Applications. ISSOIA 2018. Springer Proceedings in Physics, vol 232. Springer, Cham. https://doi.org/10.1007/978-3-030-27300-2_36
Download citation
DOI: https://doi.org/10.1007/978-3-030-27300-2_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27299-9
Online ISBN: 978-3-030-27300-2
eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)