Paper Info
Title
Data Parallel Supervised Classification Algorithms On Multispectral Images
Abstract
In remote sensing the intensities from a multispectral image are used in a classification scheme to distinguish different ground cover from each other. An example is given where different soil types are classified. A digitized complete scene from a satellite sensor consists of a large amount of data and in future image sensors the resolution and the number of spectral bands will increase even further. Data parallel computers are therefore well-suited for these types of classification algorithms. This article will focus on three supervised classified algorithms: the Maximum Likelihood, the K-Nearest Neighbor and the Backpropagation algorithm, together with their parallel implementations. They are implemented on the Connection Machine/200 in the high-level language C*. The algorithms are finally tested and compared on an image registered over western Estonia.
Year
DOI
Venue
1996
10.1142/S021800149600044X
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
Keywords
Field
DocType
data parallel algorithms, classification, satellite images, remote sensing, backpropagation, K-nearest neighbor, maximum likelihood
k-nearest neighbors algorithm,Satellite,Pattern recognition,Image sensor,Computer science,Multispectral image,Multispectral pattern recognition,Artificial intelligence,Spectral bands,Backpropagation,Statistical classification,Machine learning
Journal
Volume
Issue
ISSN
10
7
0218-0014
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Thomas Johnsson100.34
ewert bengtsson213525.36