Neural network methods for analysis of remotely-sensed satellite dataFunded under: JRC-REMSENS 4C
Experiments have been conducted on the classification of remotely-sensed satellite imagery using multi-layer perceptron neural networks. The implementation of such networks yields results of high accuracy. Training of large networks required for realistic satellite mapping applications can require several hours of computer processing, although classification can be performed very rapidly. Some indications are given of techniques to design appropriate network architectures and to control the training procedures for fast convergence.
Bibliographic Reference: Paper presented: International Space Year Conference, München (DE), March 30 - April 4, 1992
Availability: Available from (1) as Paper EN 36931 ORA
Record Number: 199210949 / Last updated on: 1994-12-02
Original language: en
Available languages: en