Abstract
An object-based strategy is presented to identify soil-sealing in urban environment using Very High Resolution (VHR) remote sensing images. A first stage of segmentation has been carried out using a watershed algorithm, and then a second stage of supervised classification has been done to classify land covers. The resulting land covers have been used to discriminate between sealed and unsealed surfaces. The selection of features and of the number of land cover classes has been guided by an exploratory clustering stage. The proposed strategy has been used to classify sealed surfaces for two single-date images. The post-classification results have been used in a Change Detection Task, in order to analyze the land take problem in a periurban area of Venezia Mestre between 2005 and 2010. The change detection results are promising, considering the good capacity to reveal changes at the characteristic dimension of small man-made structures, and they can be considered a good support to a successive photointerpretation step.
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Pugliese, L., Scarpetta, S. (2014). An Object Based Analysis Applied to Very High Resolution Remote Sensing Data for the Change Detection of Soil Sealing at Urban Scale. In: Bassis, S., Esposito, A., Morabito, F. (eds) Recent Advances of Neural Network Models and Applications. Smart Innovation, Systems and Technologies, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-319-04129-2_16
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DOI: https://doi.org/10.1007/978-3-319-04129-2_16
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