用魏尔斯特拉斯变换提升遥感图像最大似然分类精度
Comparison of Maximum Likelihood Classification Before and After Applying Weierstrass Transform
- 对遥感数据先做魏尔斯特拉斯变换再分类
- 变换后类别均值在决策空间分离度更高，准确率提升
- 适合遥感图像处理与分类算法研究者
本文通过定性与定量方法，对比在高分辨率QuickBird卫星影像上应用魏尔斯特拉斯变换前后最大似然（ML）分类的效果。最大似然分类基于经典贝叶斯定理，利用判别函数将像素分配给似然最高的类别，其关键输入为各类别的均值向量与协方差矩阵，可通过训练样本估计。该方法需满足若干前提假设。本文比较了施加魏尔斯特拉斯变换前后分类结果的差异。同时采用主成分分析（PCA）进行降维并检验波段间变异情况。结果显示，变换后类别均值在决策空间的分离程度显著提高，成为提升最大似然分类精度的主要因素。
原文摘要 · Abstract (English)
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.
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