Abstract
Detection of different regions like impervious surfaces, vegetation and water from a multispectral satellite image is a complex task. This paper introduces a novel idea for impervious surface detection from multispectral images using SURF descriptors. To determine the efficiency of the proposed system, a comparative evaluation is done with other two techniques, namely histogram based and spectral-value-based technique. The result shows that the proposed system outperforms the other two techniques in detecting impervious surfaces like buildings and vehicles with an accuracy of 80.48%. The histogram-based technique and spectral-value-based clustering obtained an accuracy of 61.89% and 68.29% respectively. However, in classifying vegetation the other two techniques outperforms SURF descriptors. The histogram based technique gives an accuracy of 86.46% and an accuracy of 94.35% is obtained by using the spectral-value-based clustering. Whereas SURF based technique gives only an accuracy of 50.71%.
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
Preview
Unable to display preview. Download preview PDF.
References
Lu, D., Weng, Q.: A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing 28(5), 823–870 (2007)
Dean, A.M., Smith, G.M.: An evaluation of per-parcel land cover mapping using maximum likelihood class probabilities. International Journal of Remote Sensing 24, 2905–2920 (2003)
Aplin, P., Atkinson, P.M., Curran, P.J.: Fine spatial resolution simulated satellite sensor imagery for land cover mapping in the United Kingdom. Remote Sensing of Environment 68, 206–216 (1999)
Schowengerdt, R.A.: On the estimation of spatial-spectral mixing with classifier likelihood functions. Pattern Recognition Letters 17, 1379–1387 (1996)
Cracknell, A.P.: Synergy in remote sensing – what’s in a pixel. International Journal of Remote Sensing 19, 2025–2047 (1998)
Binaghi, E., Brivio, P.A., Ghezzi, P., Rampini, A.: A fuzzy set accuracy assessment of soft classification. Pattern Recognition Letters 20, 935–948 (1999)
Janssen, L.F., Molenaar, M.: Terrain objects, their dynamics and their monitoring by integration of GIS and remote sensing. IEEE Transactions on Geoscience and Remote Sensing 33, 749–758 (1995)
Foody, G.M., Cox, D.P.: Sub-pixel land cover composition estimation using a linear mixture model and fuzzy membership functions. International Journal of Remote Sensing 15, 619–631 (1994)
Foody, G.M.: Image classification with a neural network: from completely-crisp to fully-fuzzy situation. In: Atkinson, P.M., Tate, N.J. (eds.) Advances in Remote Sensing and GIS Analysis, pp. 17–37. John Wiley and Sons, New York (1999)
Paola, J.D., Schowengerdt, R.A.: A review and analysis of back propagation neural networks for classification of remotely sensed multispectral imagery. International Journal of Remote Sensing 16, 3033–3058 (1995)
Wang, L., Sousa, W.P., Gong, P., Biging, G.S.: Comparison of IKONOS and QuickBird images for mapping mangrove species on the Caribbean coast of panama. Remote Sensing of Environment 91, 432–440 (2004)
Fisher, P.: The pixel: a snare and a delusion. International Journal of Remote Sensing 18, 679–685 (1997)
Schmitt, D., Mccoy, N.: Object Classification and Localization Using SURF Descriptors. CS 229 Final Project (2011)
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: SURF: Speeded Up Robust Features. Computer Vision and Image Understanding (CVIU) 110(3), 346–359 (2008)
Aplin, P., Atkinson, P.M., Curran, P.J.: Per-field classification of land use using the forthcoming very fine spatial resolution satellite sensors: problems and potential solutions. In: Atkinson, P.M., Tate, N.J. (eds.) Advances in Remote Sensing and GIS Analysis, pp. 219–239. John Wiley and Sons, New York (1999)
Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I., Heynen, M.: Multiresolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry & Remote Sensing 58, 239–258 (2004)
Bloch, I.: Information combination operators for data fusion: a comparative review with classification. IEEE Transactions on Systems, Man, and Cybernetics 26, 52–67 (1996)
Foody, G.M.: Status of land cover classification accuracy assessment. Remote Sensing of Environment 80, 185–201 (2002)
Sarma, T.H., Viswanath, P., Reddy, B.E.: Single pass kernel k-means clustering method. Sadhan 38, Part 3, 407–419 (2013)
Thomas, N., Hendrix, C., Congalton, R.G.: A comparison of urban mapping methods using high-resolution digital imagery. Photogrammetric Engineering and Remote Sensing 69, 963–972 (2003)
Walter, V.: Object-based classification of remote sensing data for change detection. ISPRS Journal of Photogrammetry & Remote Sensing 58, 225–238 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Paulose, A., M., S., V., H. (2014). Impervious Surface Detection from Multispectral Images Using Surf. In: Hsu, R.CH., Wang, S. (eds) Internet of Vehicles – Technologies and Services. IOV 2014. Lecture Notes in Computer Science, vol 8662. Springer, Cham. https://doi.org/10.1007/978-3-319-11167-4_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-11167-4_24
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11166-7
Online ISBN: 978-3-319-11167-4
eBook Packages: Computer ScienceComputer Science (R0)