Comparison of Maximum Likelihood Classification Before and After Applying Weierstrass Transform

TL;DR

This study compares the accuracy of Maximum Likelihood Classification on multispectral data before and after applying the Weierstrass Transform, demonstrating improved class separation and accuracy post-transform.

Contribution

The paper introduces the application of the Weierstrass Transform to enhance ML classification accuracy on satellite imagery.

Findings

Weierstrass Transform improves class separation in ML classification.

Classification accuracy increases after applying the Weierstrass Transform.

Principal Component Analysis aids in dimension reduction and variation analysis.

Abstract

The aim of this paper is to use Maximum Likelihood (ML) Classification on multispectral data by means of qualitative and quantitative approaches. Maximum Likelihood is a supervised classification algorithm which is based on the Classical Bayes theorem. It makes use of a discriminant function to assign pixel to the class with the highest likelihood. Class means vector and covariance matrix are the key inputs to the function and can be estimated from training pixels of a particular class. As Maximum Likelihood need some assumptions before it has to be applied on the data. In this paper we will compare the results of Maximum Likelihood Classification (ML) before apply the Weierstrass Transform and apply Weierstrass Transform and will see the difference between the accuracy on training pixels of high resolution Quickbird satellite image. Principle Component analysis (PCA) is also used for dimension reduction and also used to check the variation in bands. The results shows that the separation between mean of the classes in the decision space is to be the main factor that leads to the high classification accuracy of Maximum Likelihood (ML) after using Weierstrass Transform than without using it.