2013
DOI: 10.5721/eujrs20134637
|View full text |Cite
|
Sign up to set email alerts
|

Comparison of standard maximum likelihood classification and polytomous logistic regression used in remote sensing

Abstract: Discriminant analysis, referred to as maximum likelihood classification within popular remote sensing software packages, is a common supervised technique used by analysts. Polytomous logistic regression (PLR), also referred to as multinomial logistic regression, is an alternative classification approach that is less restrictive, more flexible, and easy to interpret. To assess the utility of PLR in image classification, we compared the results of 15 classifications using independent validation datasets, estimat… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
45
0

Year Published

2015
2015
2023
2023

Publication Types

Select...
9

Relationship

1
8

Authors

Journals

citations
Cited by 57 publications
(45 citation statements)
references
References 40 publications
0
45
0
Order By: Relevance
“…In order to select the proper set of variables, a GLM is adopted, where the depended (classification) variable is the probability that a sub-cluster of pixels w k;i represents a missing plant. More in detail, a Binary MultivariateLogistic Regression (BMLR) model (Glonek & McCullagh, 1995) was selected which uses the logit link-function, the logarithm of odds ratio (Hogland, Billor, & Anderson, 2013).…”
Section: Missing Plant Detectionmentioning
confidence: 99%
“…In order to select the proper set of variables, a GLM is adopted, where the depended (classification) variable is the probability that a sub-cluster of pixels w k;i represents a missing plant. More in detail, a Binary MultivariateLogistic Regression (BMLR) model (Glonek & McCullagh, 1995) was selected which uses the logit link-function, the logarithm of odds ratio (Hogland, Billor, & Anderson, 2013).…”
Section: Missing Plant Detectionmentioning
confidence: 99%
“…Finally, all the values associated with each node in the new vector are equal to the shortest distance to the seed. In order to select the threshold for the seed, three classes with maximum likelihood measure [16] and for the other pixels one class with maximum likelihood measure are considered. Then the average of the shortest path for the pixels for which the specified class is one of the three classes considered for seed is calculated and displayed by b. Interval [ ] is used for the second stage segmentation.…”
Section: Shortest Path Methodsmentioning
confidence: 99%
“…Hogland et al (2013) [46] discuss the "desirable qualities" of logistic regression underscoring the rather unrestrictive model assumptions, the model's incorporation of both categorical and continuous variables into the classification scheme, ease of model comparisons, and a focus on direct modeling of class probabilities. Environmental variables have been paired with logistic regression to identify distributions of land cover [47] but coupled with multispectral data and derivatives, logistic regression has been utilized to classify land cover [48], identify areas of advancing land degradation [49], and mapped burned lands [50].…”
Section: Review Of Techniques For Remote Sensing Cropland In Savanna mentioning
confidence: 99%