Fuzzy ARTMAP — A Neural Classifier for Multispectral Image Classification

Sucharita Gopal and Manfred Fischer
Additional contact information
Sucharita Gopal: Boston University

Chapter 7 in GeoComputational Modelling, 2001, pp 165-194 from Springer

Abstract: Abstract Spectral pattern recognition deals with classifications that utilize pixel-by-pixel spectral information from satellite imagery. The literature on neural network applications in this area is relatively new, dating back only about six to seven years. The first studies established the feasibility of error-based learning systems such as backpropagation (see Key, Maslanik and Schweiger 1989; McClellan et al. 1989; Benediktsson, Swain and Ersoy 1990; Hepner et al. 1990). Subsequent studies analyzed backpropagation networks in more detail and compared them to standard statistical classifiers such as the Gaussian maximum likelihood (see Bischof; Schneider and Pinz 1992; Kanellopoulos, Wilkinson and Mégier 1993; Fischer et al. 1994).

Keywords: Adaptive Weight; Adaptive Resonance Theory; Fuzzy ARTMAP; Neural Classifier; ARTa Category (search for similar items in EconPapers)
Date: 2001
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Chapter: Fuzzy ARTMAP — A Neural Classifier for Multispectral Image Classification (1997)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:adspcp:978-3-662-04637-1_7

Ordering information: This item can be ordered from
http://www.springer.com/9783662046371

DOI: 10.1007/978-3-662-04637-1_7

Access Statistics for this chapter

More chapters in Advances in Spatial Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().