Spectral classification of building materials based on SPA feature extreme learning machine

Spectral classification of building materials based on SPA feature extreme learning machine

Spectral classification of building materials is an important research area that can help us effectively identify and classify different types of building materials. In this article, we will introduce how to use the SPA feature Extreme Learning Machine (ELM) to implement the classification of building material spectra and provide the corresponding MATLAB code.

The SPA feature extreme learning machine is a machine learning algorithm based on artificial neural networks, which has fast training and good generalization capabilities when processing large-scale data sets. The core idea of ​​this algorithm is to randomize the weights and biases of the hidden layer and use the analytical solution to calculate the weight of the output layer. This randomization feature makes ELM very fast during the training process and can handle noise and outliers better.

The following is the code for using MATLAB to implement spectral classification of building materials:

% 步骤1：准备数据
% 假设我们有N个建筑材料样本，每个样本有M个光谱特征
% 将光谱特征保存在一个N×M的矩阵中，命名为X
% 将每个样本的类别标签保存在一个N×1的向量中，命名为Y

% 步骤2：数据预处理
% 可以根据需要对数据进行预处理，例如归一化、标准化等

% 步骤3：将数据划分为训练集和测试集
% 可以使用交叉验证或随机划分的方法将数据划分为训练集和测试集