Urban land cover and land use classification of an informal settlement area using the open-source knowledge-based system InterIMAGE

Abstract

This study uses the InterIMAGE system and imagery from the QuickBird sensor for land cover and land use classification at two test sites with informal settlements in the metropolis of São Paulo, Brazil. InterIMAGE is an open source and free access system for knowledge-based image classification. Within InterIMAGE human knowledge is represented as a semantic net and by user-defined rules based on the paradigms of object-oriented image analysis. In the land cover classification step, a genetic algorithm was used for determining appropriate segmentation parameters. For the description of the land cover classes in terms of features and thresholds, a strategy combining machine learning algorithms and a semantic net was elaborated. Based on the land cover classifications, the land use classifications were carried out considering the urban blocks of the test sites as the analysis units. Customized features related to the composition and geometrical structures of the land cover objects within these blocks were used for the description of the land use classes. The proposed methodology has been shown to be efficient for the automatic mapping of the land cover and land use in complex urban areas. The land cover classifications achieved overall accuracies above 70 percent and Kappa indexes above 0.65. Referring to the land use classifications, overall accuracies above 87 percent and Kappa indexes above 0.71 were obtained. This study has explored the main functionalities of the InterIMAGE system, presenting its potential for object-based and knowledge-based image classification.