Logistic Regression for Feature Selection and Soft Classification of Remote Sensing Data

Feature selection is a key task in remote sensing data processing, particularly in case of classification from hyperspectral images. A logistic regression (LR) model may be used to predict the probabilities of the classes on the basis of the input features, after ranking them according to their rela...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2006-10, Vol.3 (4), p.491-494
Main Authors: Qi Cheng, Varshney, P.K., Arora, M.K.
Format: Article
Language:English
Subjects:
Classification
Data processing
Feature extraction
Feature selection
Hyperspectral imaging
Hyperspectral sensors
Information resources
Linear discriminant analysis
linear discriminant analysis (LDA)
logistic regression (LR)
Logistics
Mathematical models
Predictive models
Regression
Remote sensing
Robustness
soft classification
Tasks
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Summary:Feature selection is a key task in remote sensing data processing, particularly in case of classification from hyperspectral images. A logistic regression (LR) model may be used to predict the probabilities of the classes on the basis of the input features, after ranking them according to their relative importance. In this letter, the LR model is applied for both the feature selection and the classification of remotely sensed images, where more informative soft classifications are produced naturally. The results indicate that, with fewer restrictive assumptions, the LR model is able to reduce the features substantially without any significant decrease in the classification accuracy of both the soft and hard classifications
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2006.877949