Comparison Analysis of Recognition Algorithms of Forest-Cover Objects on Hyperspectral Air-Borne and Space-Borne Images

The basic model for the recognition of natural and anthropogenic objects using their spectral and textural features is described in the problem of hyperspectral air-borne and space-borne imagery processing. The model is based on improvements of the Bayesian classifier that is a computational procedu...

Full description

Saved in:
Bibliographic Details
Published in:Izvestiya. Atmospheric and oceanic physics 2017-12, Vol.53 (9), p.1132-1141
Main Authors: Kozoderov, V. V., Kondranin, T. V., Dmitriev, E. V.
Format: Article
Language:English
Subjects:
Algorithms
Anthropogenic factors
Bayesian analysis
Classifiers
Climatology
Computer applications
Decision making
Earth and Environmental Science
Earth Sciences
Empirical analysis
Empirical orthogonal functions
Euclidean geometry
Geophysics/Geodesy
Imagery
Learning algorithms
Machine learning
Methods and Means of Processing and Interpretation of Space Information
Object recognition
Orthogonal functions
Pattern recognition
Probability theory
Support vector machines
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The basic model for the recognition of natural and anthropogenic objects using their spectral and textural features is described in the problem of hyperspectral air-borne and space-borne imagery processing. The model is based on improvements of the Bayesian classifier that is a computational procedure of statistical decision making in machine-learning methods of pattern recognition. The principal component method is implemented to decompose the hyperspectral measurements on the basis of empirical orthogonal functions. Application examples are shown of various modifications of the Bayesian classifier and Support Vector Machine method. Examples are provided of comparing these classifiers and a metrical classifier that operates on finding the minimal Euclidean distance between different points and sets in the multidimensional feature space. A comparison is also carried out with the “ K -weighted neighbors” method that is close to the nonparametric Bayesian classifier.
ISSN:0001-4338
1555-628X
DOI:10.1134/S0001433817090171