Road detection over informal settlements in a suburban area of Sao Paulo city by using high resolution satellite image and a object-based classification approach. / Detecção da malha viária na periferia urbana de São Paulo utilizando imagens de alta resolução espacial e classificação orientada a objetos.

Rodrigo Affonso de Albuquerque Nóbrega

Uncontrolled sprawl occurring in large cities of developing countries requires intensive mapping efforts to update geodatabases. The intense urbanization process experienced since the 70s in Sao Paulo city illustrates very well the reported scenario. Despite aerial data and, more recent, high spatial resolution satellite data which have been employed as basis for mapping, the need for precise, faster and cheaper mapping efforts is real. In this sense, automated classification of high resolution imagery has demonstrated unsatisfactory results when traditional per-pixel classifiers are used, especially for urban areas. The increasing success of object-based classification has stimulated researchers to create new methodologies to overcome this shortcoming of traditional approaches. The object-based image classifications idea is extract object-primitives from images and then use their information to compose rules and strategies to be applied on the classification process. Beyond the spectral analysis, geometric, and contextual analysis are also addressed on object-based classification. This work reports the use of object-based image classification applied on road detection over the suburban area of Sao Paulo city. Informal settlements compose the most part of the study area and the transportation network reflects the unplanned occupation. Roads are geometrically irregular and with different kind of pavements. Detecting roads based on these characteristics was the biggest challenge faced here, and this work hypothesizes object-based classification can be used to. The methodology presented employs an IKONOS II data. At first, principal components and segmentation were computed and then auxiliary data for impervious surface and bare soil areas were previously calculated from customized features. In short, based on geometric information as width, length, asymmetry, area, and more, objects were elected as road and then analyzed through contextual information as paved road or unpaved road. Results were analyzed under three different ways: 1) visual inspection, where the adherence between extracted road and real ones provided a good indicator for qualitative analysis ; 2) classification accuracy, by comparing detected road areas and referential ones, which provided statistical parameters for quality as omission and commission error ; 3) linear comparative analysis, which provided parameters as correctness and completeness using referential lines and lines arose from extracted areas based on mathematical morphology tools. Regarding the high degree of heterogeneity of features present on study area, the overall accuracy reached is good. Despite the methodology did not produce a road map, the results shown the combined use of high resolution multi-spectral imagery and object-based classification can effectively mine road features, producing significant information to support decision makers.

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