OPTIMIZING OBJECT-BASED CLASSIFICATION IN URBAN ENVIRONMENTS USING VERY HIGH RESOLUTION GEOEYE-1 IMAGERY

Monitoring Seasonal Hydrological Dynamics of Minerotrophic Peatlands Using Multi-Date GeoEye-1 Very High Resolution Imagery and Object-Based Classification

The La Grande River watershed, located in the James Bay region (54°N, Quebec, Canada), is a major contributor to the production of hydroelectricity in the province. Peatlands cover up to 20% of the terrestrial environment in this region. Their hydrological behavior is not well understood. The present study is part of a multidisciplinary project which is aimed at analyzing the hydrological proce...

Urban classification by pixel and object-based approaches for very high resolution imagery

Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery

This study evaluates and compares the performance of four machine learning classifiers—support vector machine (SVM), normal Bayes (NB), classification and regression tree (CART) and K nearest neighbor (KNN)—to classify very high resolution images, using an object-based classification procedure. In particular, we investigated how tuning parameters affect the classification accuracy with differen...

Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery

a School of Geographical Sciences and Urban Planning, Arizona State University, P.O. Box 875302, Tempe, AZ 85287-5302, United States b Global Institute of Sustainability, Arizona State University, PO Box 875402, Tempe, AZ 85287, United States c Department of Geography, Geology, and Anthropology, Indiana State University, Terre Haute, IN 47809, United States d Potsdam-Institute for Climate Impac...

Mapping Urban Tree Species Using Very High Resolution Satellite Imagery: Comparing Pixel-Based and Object-Based Approaches

We assessed the potential of multi-spectral GeoEye imagery for biodiversity assessment in an urban context in Bangalore, India. Twenty one grids of 150 by 150 m were randomly located in the city center and all tree species within these grids mapped in the field. The six most common species, collectively representing 43% of the total trees sampled, were selected for mapping using pixel-based and...