Paper Info
Title
Characterizing land cover from X-band COSMO-SkyMed images by neural networks
Abstract
The launch of last-generation satellites (COSMO-SkyMed and TerraSAR-X), equipped with X-band sensors acquiring images with a very high spatial resolution, has opened up new challenges in the field of SAR image processing for remote sensing applications. In this work, a set of Spotlight and Stripmap COSMO-Skymed images taken the Tor Vergata-Frascati test site was considered to investigate on the potential of this type of data in characterizing sub-urban areas by exploiting both amplitude and phase information contained in the radar return. In particular, this contribution deals with the development of a pixel based classification technique based on Multi-Layer Perceptron (MLP) Neural Networks (NN). The results have been compared with a land cover map of the same area, achieved by means of a different neural network algorithm exploiting the information carried by the eight bands of WorldView-2 satellite.
Year
DOI
Venue
2011
10.1109/JURSE.2011.5764716
Urban Remote Sensing Event
Keywords
Field
DocType
geophysical image processing,image classification,multilayer perceptrons,remote sensing,mlp,nn,sar image processing,tor vergata-frascati test site,worldview-2 satellite,x-band cosmo-skymed images,amplitude information,characterizing land cover,multilayer perceptron,neural networks,phase information,pixel based classification technique,remote sensing applications,multi layer perceptron,neural network,asphalt,pixel,spatial resolution,artificial neural networks
Radar,Computer science,Remote sensing,Image processing,Remote sensing application,Multilayer perceptron,Pixel,Contextual image classification,Image resolution,Perceptron
Conference
ISBN
Citations 
PageRank 
978-1-4244-8658-8
2
0.42
References 
Authors
7
5
Name
Order
Citations
PageRank
Chiara Pratola1346.02
Fabio Del Frate250872.43
Schiavon, G.3306.47
Domenico Solimini46515.10
Giorgio A. Licciardi5404.82