IMPROVEMENT EVALUATION ON CERAMIC ROOF EXTRACTION USING WORLDVIEW-2 IMAGERY AND GEOGRAPHIC DATA MINING APPROACH
- 1University of St. Andrews, Department of Geography and Sustainable Development, St. Andrews – Fife, United Kingdom
- 2Federal University of Rio Grande do Sul (UFRGS), Graduate Program on Urban and Regional Planning (PROPUR) Porto Alegre – RS, Brazil
- 3Federal University of ABC (UFABC), Engineering, Modeling and Applied Social Sciences Center (CECS), São Bernardo do Campo – SP, Brazi
- 4National Institute for Space Research (INPE), Image Processing Division (DPI), São José dos Campos – SP, Brazil
Keywords: Geographical Data Mining, GEOBIA, WorldView-2, Ceramic roof, C4.5, Decision Tree, Classification accuracy
Abstract. Advances in geotechnologies and in remote sensing have improved analysis of urban environments. The new sensors are increasingly suited to urban studies, due to the enhancement in spatial, spectral and radiometric resolutions. Urban environments present high heterogeneity, which cannot be tackled using pixel–based approaches on high resolution images. Geographic Object–Based Image Analysis (GEOBIA) has been consolidated as a methodology for urban land use and cover monitoring; however, classification of high resolution images is still troublesome. This study aims to assess the improvement on ceramic roof classification using WorldView-2 images due to the increase of 4 new bands besides the standard “Blue-Green-Red-Near Infrared” bands. Our methodology combines GEOBIA, C4.5 classification tree algorithm, Monte Carlo simulation and statistical tests for classification accuracy. Two samples groups were considered: 1) eight multispectral and panchromatic bands, and 2) four multispectral and panchromatic bands, representing previous high-resolution sensors. The C4.5 algorithm generates a decision tree that can be used for classification; smaller decision trees are closer to the semantic networks produced by experts on GEOBIA, while bigger trees, are not straightforward to implement manually, but are more accurate. The choice for a big or small tree relies on the user’s skills to implement it. This study aims to determine for what kind of user the addition of the 4 new bands might be beneficial: 1) the common user (smaller trees) or 2) a more skilled user with coding and/or data mining abilities (bigger trees). In overall the classification was improved by the addition of the four new bands for both types of users.