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Title: | Image based classification of slums, built-up and non-built-up areas in Kalyan and Bangalore, India |
Authors: | Ranguelova, E. |
Participants: | Weel, B. Roy, D. Kuffer, M. Pfeffer, K. Lees, M. |
Issue Date: | 2019 |
Series/Report no.: | European journal of remote sensing, Vol 52, Issue sup 1, pp.1-23. DOI: 10.1080/22797254.2018.1535838 |
Abstract: | Slums, characterized by sub-standard housing conditions, are a common in fast growing Asian cities. However, reliable and up-to-date information on their locations and development dynamics is scarce. Despite numerous studies, the task of delineating slum areas remains a challenge and no general agreement exists about the most suitable method for detecting or assessing detection performance. In this paper, standard computer vision methods – Bag of Visual Words framework and Speeded-Up Robust Features have been applied for image-based classification of slum and non-slum areas in Kalyan and Bangalore, India, using very high resolution RGB images. To delineate slum areas, image segmentation is performed as pixel-level classification for three classes: Slums, Built-up and Non-Built-up. For each of the three classes, image tiles were randomly selected using ground truth observations. A multi-class support vector machine classifier has been trained on 80% of the tiles and the remaining 20% were used for testing. The final image segmentation has been obtained by classification of every 10th pixel followed by a majority filtering assigning classes to all remaining pixels. The results demonstrate the ability of the method to map slums with very different visual characteristics in two very different Indian cities. |
URI: | http://tailieuso.tlu.edu.vn/handle/DHTL/8925 |
Source: | https://www.tandfonline.com/doi/full/10.1080/22797254.2018.1535838?scroll=top&needAccess=true |
ISSN: | 2279-7254 |
Appears in Collections: | Tài liệu mở |
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