A robust approach to global urban area extent extraction using ASAR Wide Swath Mode data

This paper stems for a research aimed at showing the potentials of ASAR Wide Swath data to extract human settlement extent at the global level. Exploiting the features characterizing human settlements in SAR images, and considering the coarse spatial resolution of the ASAR Wide Swath mode, partially compensated by the relatively large number of images acquired on the same area during one year's time, the developed technique shows that it is possible to delineate human settlements in different parts of the world with an overall accuracy better than 80%, and with commission and omission errors that are smaller than those available in current Globcover data sets, exploiting optical (MERIS) data at similar spatial resolution and more complex classification algorithms.

[1]  Alan H. Strahler,et al.  Validation of the global land cover 2000 map , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Thomas Esch,et al.  Identification and characterization of urban structures using VHR SAR data , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[3]  Martino Pesaresi,et al.  A Robust Built-Up Area Presence Index by Anisotropic Rotation-Invariant Textural Measure , 2008, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[4]  Shaun Quegan,et al.  Filtering of multichannel SAR images , 2001, IEEE Trans. Geosci. Remote. Sens..

[5]  Paolo Gamba,et al.  Spatial Indexes for the Extraction of Formal and Informal Human Settlements From High-Resolution SAR Images , 2008, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[6]  Paolo Gamba,et al.  Robust Extraction of Urban Area Extents in HR and VHR SAR Images , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[7]  M. Herold,et al.  Global Mapping of Human Settlement : Experiences, Datasets, and Prospects , 2009 .

[8]  D. Pierre,et al.  Producing global land cover maps consistent over time to respond the needs of the climate modelling community , 2011, 2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp).