The novel instruments of the COSMO-SkyMed (CSK) Earth Observation programme, offer an opportunity to explore at various resolutions the information content of X-band signal backscattered with different polarizations. In spite of their potential to render additional information about an area of interest, speckle noise and artifacts make X-band acquisitions difficult to interpret. This is a motivating scenario to explore what (semi-)automatic procedures might be able to offer. This paper is first attempt to process CSK Stripmap PingPong data using two well-known artificial neural network techniques: the supervised backpropagation multilayer perceptron and the unsupervised self-organizing map.
Penalver, M., Pratola, C., Fabrini, I., DEL FRATE, F., Schiavon, G., Solimini, D. (2012). Classification of PingPong COSMO-SkyMed imagery using supervised and unsupervised neural network algorithms. In Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International (pp.5888-5891) [10.1109/IGARSS.2012.6352269].
Classification of PingPong COSMO-SkyMed imagery using supervised and unsupervised neural network algorithms
DEL FRATE, FABIO;SCHIAVON, GIOVANNI;SOLIMINI, DOMENICO
2012-01-01
Abstract
The novel instruments of the COSMO-SkyMed (CSK) Earth Observation programme, offer an opportunity to explore at various resolutions the information content of X-band signal backscattered with different polarizations. In spite of their potential to render additional information about an area of interest, speckle noise and artifacts make X-band acquisitions difficult to interpret. This is a motivating scenario to explore what (semi-)automatic procedures might be able to offer. This paper is first attempt to process CSK Stripmap PingPong data using two well-known artificial neural network techniques: the supervised backpropagation multilayer perceptron and the unsupervised self-organizing map.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
