Advanced automated classification strategy for settlement area detection

Ulrich Michel

doi:10.1117/12.627034

28 October 2005 Advanced automated classification strategy for settlement area detection

Ulrich Michel

Proceedings Volume 5983, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology V; 598308 (2005) https://doi.org/10.1117/12.627034
Event: SPIE Remote Sensing, 2005, Bruges, Belgium

Abstract

This paper is based on the research project "State-wide acquisition of settlement area in North Rhine-Westphalia, Germany focusing on residential and industrial areas". The integrated GIS/Remote Sensing environment facilitated the combined processing of ground truth measurement, scanned topographic maps and multisensor imagery from SPOT 5, Landsat 7 and Aster satellites. In selected urban and suburban areas, methods for multisensor data fusion were developed and tested. The goal of this project is an accurate and current information layer about the urban and suburban state for North Rhine-Westphalia. The methodology is based mainly on an adapted texture and object oriented hierarchical classification approach: based on SPOT 5 imagery segments in different scales (levels) were created. These segments are the basis for a hierarchical based classification procedure. For each segment we calculated not only a texture but also the shape parameter. In addition we used the normalized vegetation index (NDVI) calculated from the multispectral satellite images to distinguish between vegetation and non vegetation areas. The GIS environment is of prime importance because it offers adapted tools for data modelling and data combination. For the verification of our results we developed a GPS based mobile application running on a windows mobile pocket pc. It allows a direct communication with our GIS environment which is necessary to import or export our datasets.

Citation Download Citation

Ulrich Michel "Advanced automated classification strategy for settlement area detection", Proc. SPIE 5983, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology V, 598308 (28 October 2005); https://doi.org/10.1117/12.627034

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