润色后面句子。高光谱影像分类的主要目标是利用待测地物的空间几何信息以及其丰富的光谱信息特征，将图像中的每个像素判别为不同的地物类别。进行高光谱图像分类的传统机器学习方法包括支持向量机（Support Vector Machine，SVM）、相关向量机（Relevance Vector Machine，RVM）、神经网络、核方法、多变量逻辑回归(Multivariable Logistic Regression，MLR)等及其变体。

The ultimate aim of high spectral image classification is to utilize the spatial and geometric information of the target object, as well as its rich spectral features, to differentiate each pixel in the image into different land cover categories. Traditional machine learning methods for high spectral image classification include Support Vector Machines (SVM), Relevance Vector Machines (RVM), neural networks, kernel methods, Multivariable Logistic Regression (MLR) and their variations.