Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Remote sensed image classification using multi-perspective neural networks
Remotely sensed imagery classification is widely used for Earth resource inventory. Due to variations of imaging conditions the signature of images and the objects on the land have no unique correspondence. This results is a great difficulty for the computer processing of remotely sensed imagery. The present authors describe a novel neural network model LEP (Learning based on Experiences and Perspectives), and its application to remote sensed image classification. Because the network properly makes use of multi-perspective data and its learning is finely tuned by experience, the classification results have been much improved.<>
Remote sensed image classification using multi-perspective neural networks
Remotely sensed imagery classification is widely used for Earth resource inventory. Due to variations of imaging conditions the signature of images and the objects on the land have no unique correspondence. This results is a great difficulty for the computer processing of remotely sensed imagery. The present authors describe a novel neural network model LEP (Learning based on Experiences and Perspectives), and its application to remote sensed image classification. Because the network properly makes use of multi-perspective data and its learning is finely tuned by experience, the classification results have been much improved.<>
Remote sensed image classification using multi-perspective neural networks
Jian Kang Wu (Autor:in) / Takagi, M. (Autor:in)
01.01.1993
465887 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Multi Class Adult Image Classification Using Neural Networks
| British Library Conference Proceedings | 2005
Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification
| Online Contents | 2017
Classification of forest land attributes using multi-source remotely sensed data
| Online Contents | 2016