Abstract
This paper presents a new method for residential areas extraction from multispectral satellite imagery. First, the image is preprocessed so that non-residential areas noises such as vegetation regions, shadows and water areas are removed. Second, the preprocessed image is classified into two categories by texture features: residential areas class and non-residential areas class. Finally, the residential areas class is refined by using a series of morphology operations. The experimental results show that this method is effective in extracting residential areas from multispectral satellite imagery.
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
Similar content being viewed by others
References
Vatsavai, R.R., Bright E., Varun C., et al.: Machine learning approaches for high-resolution urban land cover classification: a comparative study. In: International Conference and Exhibition on Computing for Geospatial Research & Application, pp. 1–10, Washington, DC, USA (2011)
Tao, C., Tan, Y., Zou, Z.R., et al.: Unsupervised detection of built-up areas from multiple high-resolution remote sensing images. IEEE Geosci. Remote Sens. Lett. 10(6), 1300–1304 (2013)
Chen, H., Tao, C., Zou, Z.R., et al.: Automatic urban area extraction using a gabor filter and high-resolution remote Sensing imagery. Geomat. Inf. Sci. Wuhan Univ. 38(9), 1063–1067 (2013)
Fauvel, M., Chanussot, J., Benediktsson, J.A.: Decision fusion for the classification of urban remote sensing images. IEEE Trans. Geosci. Remote Sens. 44(10), 2828–2838 (2006)
Benediktsson, J.A., Pesaresi, M., Amason, K.: Classification and feature extraction for remote sensing images from urban areas based on morphological transformations. IEEE Trans. Geosci. Remote Sens. 41(9), 1940–1949 (2003)
Holben, B.N.: Characteristics of maximum-value composite images from temporal avhrr data. Int. J. Remote Sens. 7(11), 1417–1434 (1986)
Ohtsu, N.: A Threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern 9(1), 62–66 (1979)
Fogel, I., Sagi, D.: Gabor filters as texture discriminator. Biol. Cybern. 61(2), 103–113 (1989)
Manjunath, B.S., Ma, W.Y.: Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Analy. Mach. Intell. 18(8), 837–842 (1996)
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2(2), 121–167 (1998)
Haralick, R.M., Shanmugam, K., Dinstein, I.: Texture features for image classification. IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973)
Acknowledgments
This work is supported by the project of science and technology of Fujian province (2016H0001), the project of Fuzhou Municipal Science and Technology Bureau (2015-G-53), and the key project of science and technology of Fujian province (2014H6006).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Xu, R., Zeng, Y., Liang, Q. (2017). A New Method for Extraction of Residential Areas from Multispectral Satellite Imagery. In: Pan, JS., Snášel, V., Sung, TW., Wang, X. (eds) Intelligent Data Analysis and Applications. ECC 2016. Advances in Intelligent Systems and Computing, vol 535. Springer, Cham. https://doi.org/10.1007/978-3-319-48499-0_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-48499-0_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48498-3
Online ISBN: 978-3-319-48499-0
eBook Packages: EngineeringEngineering (R0)