Application of MLP and RBF networks to cloud detection

W. D. Zhang, M. X. He, Man Wai Mak

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)

Abstract

This paper compares the performances of multi-layer perceptrons (MLPs) and radial basis function (RBF) networks on detecting clouds in NOAA/AVHRR images. The main results show that the RBF networks are able to handle complex atmospheric and oceanographic phenomena while the conventional rule-based systems and MLPs can not. In particular, the experimental evaluations show that the RBF networks can converge to global minima while the MLPs can only achieve this occasionally, and that classification errors made by the RBF networks decrease dramatically when the number of basis functions increases. In addition, these errors are almost identical when the number of basis functions reaches a threshold. Only on a few rare occasions when the backpropagation algorithm attains an optimal solution and the classification errors made by the MLPs be comparable to (but still larger than) the ones made by the RBF networks. However, the results show that achieving such optimal solutions is difficult. It is, therefore, concluded that the RBF networks are better than the MLPs for cloud detection.
Original languageEnglish
Title of host publicationProceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2001
Pages60-63
Number of pages4
Publication statusPublished - 1 Dec 2001
Event2001 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2001 - Hong Kong, Hong Kong
Duration: 2 May 20014 May 2001

Conference

Conference2001 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2001
Country/TerritoryHong Kong
CityHong Kong
Period2/05/014/05/01

ASJC Scopus subject areas

  • Computer Science(all)

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