The aim of this paper is to discuss the use deep neural networks (DNNs) for road detection from high-resolution aerial images. DNNs are a powerful mathematical model for information processing that has been proven to be effective to accomplish several complex tasks including coding and classification of image data. The DNN with its many trainable weights allows taking a large context into account that is essential for the difficult problem of detect roads from aerial image. Furthermore it allows to progressively combining lower-level features or concepts into more abstract and higher-level representations. Thus, we believe useful to discuss possible applications DNNs for roads detection in the present work.
An introductory study on deep neural networks for high resolution aerial images
Salerno V;SINISCALCHI, SABATO MARCO
2013-01-01
Abstract
The aim of this paper is to discuss the use deep neural networks (DNNs) for road detection from high-resolution aerial images. DNNs are a powerful mathematical model for information processing that has been proven to be effective to accomplish several complex tasks including coding and classification of image data. The DNN with its many trainable weights allows taking a large context into account that is essential for the difficult problem of detect roads from aerial image. Furthermore it allows to progressively combining lower-level features or concepts into more abstract and higher-level representations. Thus, we believe useful to discuss possible applications DNNs for roads detection in the present work.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
