Fast and Efficient Urban Extent Extraction Using ASAR Wide Swath Mode Data

This paper aims at introducing a fast and efficient approach able to extract human settlement extents using ASAR Wide Swath Mode data. The proposed approach exploits the spatial features characterizing human settlements in SAR data at a spatial resolution around 100 m, i.e., long term coherence and large backscattered power values. The joint use of multi-temporal filtering and averaging and the homogeneously high SAR return from built-up structures is the key to extract quickly and robustly human settlement extents. Although prone to commission errors in mountainous areas, the procedure proposed in this paper proved to be able to extract consistently more accurate results than existing global data sets including Globcover 2009. This was assessed by running a series of tests in different geographical areas and comparing the new and the existing products with independently extracted “urban” and “non-urban” points. The results show that ASAR data have no fewer potential than optical ones for global mapping of human settlements. Properly processed, instead, SAR data are able to provide an effective solution to the need of a global map of human settlement, useful for risk computations, climate change model inputs and population mapping, among other applications.

[1]  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.

[2]  H. Taubenbock,et al.  Monitoring of global urbanization-time series analyses for mega cities based on optical and SAR data , 2012, 2012 Second International Workshop on Earth Observation and Remote Sensing Applications.

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

[4]  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.

[5]  M. Friedl,et al.  A new map of global urban extent from MODIS satellite data , 2009 .

[6]  Paolo Gamba,et al.  A robust approach to global urban area extent extraction using ASAR Wide Swath Mode data , 2012, 2012 Tyrrhenian Workshop on Advances in Radar and Remote Sensing (TyWRRS).

[7]  Peng Gong,et al.  A comparison of spatial feature extraction algorithms for land-use classification with SPOT HRV data , 1992 .

[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).

[9]  Martino Pesaresi,et al.  Toward Global Automatic Built-Up Area Recognition Using Optical VHR Imagery , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[10]  F. Lindsay,et al.  Dynamics of urban growth in the Washington DC metropolitan area, 1973-1996, from Landsat observations , 2000 .

[11]  Roland Klees,et al.  SAR interferometry on a very long time scale: a study of the interferometric characteristics of man-made features , 1999, IEEE Trans. Geosci. Remote. Sens..

[12]  Stuart Barr,et al.  Reducing structural clutter in land cover classifications of high spatial resolution remotely-sensed images for urban land use mapping , 2000 .

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

[14]  Andreas Schenk,et al.  Characterization of Land Cover Types in TerraSAR-X Images by Combined Analysis of Speckle Statistics and Intensity Information , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Limin Yang,et al.  COMPLETION OF THE 1990S NATIONAL LAND COVER DATA SET FOR THE CONTERMINOUS UNITED STATES FROM LANDSAT THEMATIC MAPPER DATA AND ANCILLARY DATA SOURCES , 2001 .

[16]  Damien Sulla-Menashe,et al.  MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets , 2010 .

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

[18]  Martino Pesaresi,et al.  Statistical analysis of anisotropic rotation-invariant textural measurements of human settlements from multitemporal SAR data , 2011, 2011 Joint Urban Remote Sensing Event.

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

[20]  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.

[21]  Curt H. Davis,et al.  A combined fuzzy pixel-based and object-based approach for classification of high-resolution multispectral data over urban areas , 2003, IEEE Trans. Geosci. Remote. Sens..

[22]  C. Elvidge,et al.  Using nighttime DMSP/OLS images of city lights to estimate the impact of urban land use on soil resources in the United States , 1997 .

[23]  Uwe Stilla,et al.  Model-Based Interpretation of High-Resolution SAR Images of Buildings , 2008, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.