Nowadays, deep learning (DL) finds application in a large number of scientific fields, among which the estimation and enhancement signals disrupted by noise different natures. In this article, we address problem interferometric parameters from synthetic aperture radar (SAR) data. particular, combine convolutional neural networks together with concept residual to define novel architecture, named...

Synthetic aperture radar tomography (TomoSAR) has been extensively employed in 3-D reconstruction dense urban areas using high-resolution SAR acquisitions. Compressive sensing (CS)-based algorithms are generally considered as the state-of-the art super-resolving TomoSAR, particular single look case. This superior performance comes at cost of extra computational burdens, because sparse reconstru...

multilayer bach propagation neural networks have been considered by researchers. despite their outstanding success in managing contact between input and output, they have had several drawbacks. for example the time needed for the training of these neural networks is long, and some times not to be teachable. the reason for this long time of teaching is due to the selection unsuitable network par...

Article history: Received 8 July 2008 Revised 12 March 2009 Available online 3 May 2009 This paper analyzes the problem of learning the structure of a Bayes net in the theoretical framework of Gold’s learning paradigm. Bayes nets are one of the most prominent formalisms for knowledge representation and probabilistic and causal reasoning. We follow constraint-based approaches to learning Bayes n...