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Improved Support Vector Machine Enabled Radial Basis Function and Linear Variants for Remote Sensing Image

Abdul Razaque¹, Mohamed Ben Haj Frej², Muder Almi'ani³

¹Department of Computer Engineering and Information Security, International Information Technology University, Almaty 050040, Kazakhstan.

Sensors (Basel, Switzerland)

|July 2, 2021

Summary

This summary is machine-generated.

Improved Support Vector Machine (SVM) variants, specifically SVM-enabled radial basis function (RBF) and SVM-Linear, enhance land use classification accuracy in remote sensing. These advanced methods outperform traditional algorithms and offer superior generalization performance.

Keywords:

image classification improved SVM-Linear variant improved SVM-RBF variant remote sensing support vector machine

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Area of Science:

Remote Sensing
Geospatial Analysis
Machine Learning

Background:

Remote sensing is crucial for land cover and land use analysis.
Classification accuracy heavily influences the reliability of remote sensing results.
Traditional statistical classifiers face limitations due to data distribution constraints.

Purpose of the Study:

To propose and implement improved Support Vector Machine (SVM) variants for land use classification.
To evaluate the impact of parameter optimization on classification accuracy using cross-validation.
To assess the generalization performance of the proposed SVM variants.

Main Methods:

Development of improved SVM-enabled radial basis function (SVM-RBF) and SVM-Linear classifiers.
Application of cross-validation for parameter optimization and accuracy assessment.
Comparison with traditional Maximum Likelihood Classifier (MLC) and Minimum Distance Classifier (MDC), and state-of-the-art algorithms.

Main Results:

The proposed SVM-RBF and SVM-Linear variants demonstrate superior accuracy compared to traditional and state-of-the-art methods.
The improved SVM variants exhibit outstanding generalization performance, addressing overfitting and underfitting.
Enhanced reliability and fault-tolerance were observed in the proposed classification approaches.

Conclusions:

Improved SVM-RBF and SVM-Linear offer a significant advancement for remote sensing land use classification.
These methods overcome the limitations of traditional classifiers by mitigating statistical distribution constraints.
The proposed variants provide more accurate, reliable, and robust land use classification solutions.