The paper deals with the problem of reconstructing the height of forests from polarimetric/multi-baseline SAR data. The approach consists of optimizing an objective functional defined as the distance between the measured data and the data predicted by the model at the actual estimate of the unknowns. We indicate the role of global optimization on the performance of the forest height reconstruction algorithm. As global optimizer, a multilevel single-linkage method, which incorporates a local optimization into the global search, is exploited, thus offering computational efficiency and reliability. The performance of the method are illustrated against numerically simulated data.
A novel optimization approach to forest height reconstruction from multi-baseline data / Capozzoli, Amedeo; D'Elia, Giuseppe; Liseno, Angelo; A., Moreira; K. P., Papathanassiou. - ELETTRONICO. - (2007), pp. 5037-5040. (Intervento presentato al convegno IEEE Geoscience and Remote Sensing Symposium (IGARSS) tenutosi a Barcelona, Spain nel Jul. 23-27 2007) [10.1109/IGARSS.2007.4423993].
A novel optimization approach to forest height reconstruction from multi-baseline data
CAPOZZOLI, AMEDEO;D'ELIA, GIUSEPPE;LISENO, ANGELO;
2007
Abstract
The paper deals with the problem of reconstructing the height of forests from polarimetric/multi-baseline SAR data. The approach consists of optimizing an objective functional defined as the distance between the measured data and the data predicted by the model at the actual estimate of the unknowns. We indicate the role of global optimization on the performance of the forest height reconstruction algorithm. As global optimizer, a multilevel single-linkage method, which incorporates a local optimization into the global search, is exploited, thus offering computational efficiency and reliability. The performance of the method are illustrated against numerically simulated data.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
