The goal of this work is to investigate on the enhancement, in terms of accuracy and number of classes, of the vegetation mapping through the joint use of multi-sensors data. Several stacks of Spotlight COSMO-SkyMed, acquired both in HH and VV polarization, and Multispectral World-View2 images, taken in the same or different seasons, have been compounded and exploited to identify six types of natural surfaces by means of a Neural Network classifier. While the information content of the eight bands of the multispectral data may be sufficient to discriminate the classes of interest, the single polarization of each SAR image has to be integrated by extracting further features, such as textural parameters. The assessment of the provided vegetation maps has been carried out in terms of per class accuracy, overall accuracy and K coefficient. The achieved results demonstrate the improvement of the classifications obtained by fusing more information from multi-sensors acquisitions.
Pratola, C., Licciardi, G., DEL FRATE, F., Schiavon, G., Solimini, D. (2012). Fusion of VHR multispectral and X-band SAR data for the enhancement of vegetation maps. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2012 (pp.6793-6796) [10.1109/IGARSS.2012.6352604].
Fusion of VHR multispectral and X-band SAR data for the enhancement of vegetation maps
DEL FRATE, FABIO;SCHIAVON, GIOVANNI;SOLIMINI, DOMENICO
2012-01-01
Abstract
The goal of this work is to investigate on the enhancement, in terms of accuracy and number of classes, of the vegetation mapping through the joint use of multi-sensors data. Several stacks of Spotlight COSMO-SkyMed, acquired both in HH and VV polarization, and Multispectral World-View2 images, taken in the same or different seasons, have been compounded and exploited to identify six types of natural surfaces by means of a Neural Network classifier. While the information content of the eight bands of the multispectral data may be sufficient to discriminate the classes of interest, the single polarization of each SAR image has to be integrated by extracting further features, such as textural parameters. The assessment of the provided vegetation maps has been carried out in terms of per class accuracy, overall accuracy and K coefficient. The achieved results demonstrate the improvement of the classifications obtained by fusing more information from multi-sensors acquisitions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
