Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/79789
Title: A Random Forests classification method for urban land-use mapping integrating spatial metrics and texture analysis
Authors: Hernandez, IER 
Shi, WZ 
Issue Date: 2018
Publisher: Taylor & Francis
Source: International journal of remote sensing, 2018, v. 39, no. 4, p. 1175-1198 How to cite?
Journal: International journal of remote sensing 
Abstract: Rapid urban growth in developing countries is causing a great number of urban planning problems. To control and analyse this growth, new and better methods for urban land use mapping are needed. This article proposes a new method for urban land-use mapping, which integrates spatial metrics and texture analysis in an object-based image analysis classification. A high-resolution satellite image was used to generate spatial and texture metrics from the machine learning algorithm of Random Forests land-cover classification. The most meaningful spatial indices were selected by visual inspection and then combined with the image and texture values to generate the classification. The proposed method for land-use mapping was tested using a 10-fold cross-validation scheme, achieving an overall accuracy of 92.3% and a kappa coefficient of 0.896. These steps produced an accurate model of urban land use, without the use of any census or ancillary data, and suggest that the combined use of spatial metrics and texture is promising for urban land-use mapping in developing countries. The maps produced can provide the land-use data needed by urban planners for effective planning in developing countries.
URI: http://hdl.handle.net/10397/79789
ISSN: 0143-1161
EISSN: 1366-5901
DOI: 10.1080/01431161.2017.1395968
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