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.

翻译：本文旨在通过定性和定量方法对多光谱数据应用最大似然分类。最大似然是一种基于经典贝叶斯定理的监督分类算法，它利用判别函数将像素分配给具有最高似然度的类别。类别均值向量和协方差矩阵是该函数的关键输入，可从特定类别的训练像素中估计得出。由于最大似然在应用于数据前需要满足某些假设，本文将对应用魏尔斯特拉斯变换前后的最大似然分类结果进行比较，并观察高分辨率Quickbird卫星影像训练像素上分类精度的差异。主成分分析亦用于降维及检验波段间的变异。结果表明，决策空间中类别均值间的分离度是导致使用魏尔斯特拉斯变换后最大似然分类精度高于未使用情况的主要因素。