|Support type:||Scholarships in Brazil - Scientific Initiation|
|Effective date (Start):||May 01, 2016|
|Effective date (End):||April 30, 2017|
|Field of knowledge:||Physical Sciences and Mathematics - Geosciences - Geodesy|
|Principal researcher:||Tatiana Sussel Gonçalves Mendes|
|Grantee:||Andressa Nalú Hernandes|
|Home Institution:||Instituto de Ciência e Tecnologia (ICT). Universidade Estadual Paulista (UNESP). Campus de São José dos Campos. São José dos Campos , SP, Brazil|
Researches on the mapping of impervious surfaces from remotely sensed data has attracted interest since the 1970s. In the urban context, the study of impervious surfaces is highly relevant and can contribute to urban and environmental planning and resource management. The available of high resolution images has motivated and increased the interest in studies that focus on urban environment, but the high detail provided by these images present challenges for image processing techniques in order to classify urban scenes, such as: confusion in spectral response between different types of land cover; high variability of pixels within the same land cover class; and shadows caused by aboveground objects. One way to overcome these challenges is incorporate additional information in the classification process, for example, Airborne Laser Scanning (ALS) data. In this sense, this research proposes the application of Support Vector Machine (SVM) for to classify urban impervious surface from the integration of ALS data and high resolution aerial images.