Statistical analysis and neuro-fuzzy classification of polarimetric SAR images of urban areas

There is a rapidly growing need for technologies that will enable the monitoring of the world's natural resources and urban assets, and managing exposure to natural and human-made (i.e. technological) risk. This need is driven by continued, geographically disproportionate urbanization, inherent vulnerability and exposure to risk therein and the stress placed on natural resource capacity. Consider that about 74% of the world's population in developed countries lives in urban regions that are tectonically active, near the coast and/or within river floodplains, deltas and alluvial systems. The growing need for risk assessment and environmental management represents an enormous scientific challenge.

[1]  I. Kanellopoulos,et al.  Urban land use mapping with multi-spectral and SAR satellite data using neural networks , 1997, IGARSS'97. 1997 IEEE International Geoscience and Remote Sensing Symposium Proceedings. Remote Sensing - A Scientific Vision for Sustainable Development.

[2]  Robert A. Schowengerdt,et al.  A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification , 1995, IEEE Trans. Geosci. Remote. Sens..

[3]  P. Gamba,et al.  An efficient neural classification chain of SAR and optical urban images , 2001 .

[4]  P. D. Heermann,et al.  Classification of multispectral remote sensing data using a back-propagation neural network , 1992, IEEE Trans. Geosci. Remote. Sens..

[5]  F. Dell'Acqua,et al.  Recognition of urban structures in multiband data by means of ART networks , 1998, IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174).

[6]  Pierfrancesco Lombardo,et al.  Optimum detection and segmentation of oil-slicks using polarimetric SAR data , 2000 .

[7]  D. J. Weydahl,et al.  Combining ERS-1 SAR with optical satellite data over urban areas , 1995, 1995 International Geoscience and Remote Sensing Symposium, IGARSS '95. Quantitative Remote Sensing for Science and Applications.

[8]  Pierfrancesco Lombardo,et al.  Simultaneous mean and texture edge detection in SAR clutter , 1996 .

[9]  Kun-Shan Chen,et al.  Classification of multifrequency polarimetric SAR imagery using a dynamic learning neural network , 1996, IEEE Trans. Geosci. Remote. Sens..

[10]  B. Dousset Synthetic aperture radar imaging of urban surfaces: a case study , 1995, 1995 International Geoscience and Remote Sensing Symposium, IGARSS '95. Quantitative Remote Sensing for Science and Applications.

[11]  Mario Caetano,et al.  Using Artificial Recurrent Neural Nets to Identify Spectral and Spatial Patterns for Satellite Imagery Classification of Urban Areas , 1997 .

[12]  P. Lombardo,et al.  Statistical analysis of multipolarisation/multifrequency SAR images of the sea surface , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[13]  Pierfrancesco Lombardo,et al.  Estimation of texture parameters in K-distributed clutter , 1994 .

[14]  Zong-Guo Xia Applications of multi-frequency, multi-polarization and multi-incident angle SAR systems in urban land use and land cover mapping , 1996, IGARSS '96. 1996 International Geoscience and Remote Sensing Symposium.