Neural-network-based classifier applied to real-world aerial images

Research output: Contribution to conferencePaperpeer-review

2 Scopus citations

Abstract

The classification and recognition of real-world aerial images, independently of their position and orientation, by using neural network are discussed. Invariance feature spaces which have been used in conjunction with neural nets are not invariant to all possible transformations and required an extensive computational preprocessing. In the proposed method the invariance is achieved by training a Neural Network (NN) with a large number of appropriate distorted scene samples. The performance of the neural network classifier is compared with the classical correlation based techniques. Invariant classification of shifted and rotated real scene image is shown to be feasible.

Original languageEnglish
Pages4216-4219
Number of pages4
StatePublished - 1 Dec 1994
EventProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA
Duration: 27 Jun 199429 Jun 1994

Conference

ConferenceProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
CityOrlando, FL, USA
Period27/06/9429/06/94

ASJC Scopus subject areas

  • Software

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