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%.
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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
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DOI: https://doi.org/10.1007/978-3-319-11167-4_24
Publisher Name: Springer, Cham
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