（1）参考光谱：以图像中已知类型的区域为参考光谱，将区域中光谱的几何平均向量作为类中心。 （2）计算未知像元与各类中心的夹角（相关系数）。 （3）通过多次实验分别对不同的类别设置不同的阙值。若设置的阈值大于（小于）计算得出的光谱角度（相关系数），则不对该像元进行分类。 （4）将给未知像元分类到夹角最小（系数最大）的类别中去。用以上分类步骤实现的光谱角匹配的C语言或者C++代码实例有吗

以下是一个简单的用C++实现的光谱角匹配代码示例：

#include <iostream>
#include <cmath>
using namespace std;

// 计算向量的几何平均值
void geometricMean(double *vector, int size, double *result) {
    double product = 1.0;
    for (int i = 0; i < size; i++) {
        product *= vector[i];
    }
    double power = 1.0 / size;
    *result = pow(product, power);
}

// 计算向量的欧几里得范数
double euclideanNorm(double *vector, int size) {
    double sum = 0.0;
    for (int i = 0; i < size; i++) {
        sum += vector[i] * vector[i];
    }
    return sqrt(sum);
}

// 计算两个向量的夹角余弦值
double cosineSimilarity(double *vector1, double *vector2, int size) {
    double dotProduct = 0.0;
    for (int i = 0; i < size; i++) {
        dotProduct += vector1[i] * vector2[i];
    }
    double norm1 = euclideanNorm(vector1, size);
    double norm2 = euclideanNorm(vector2, size);
    return dotProduct / (norm1 * norm2);
}

// 计算两个向量的相关系数
double correlationCoefficient(double *vector1, double *vector2, int size) {
    double mean1 = 0.0;
    double mean2 = 0.0;
    for (int i = 0; i < size; i++) {
        mean1 += vector1[i];
        mean2 += vector2[i];
    }
    mean1 /= size;
    mean2 /= size;
    double numerator = 0.0;
    double denominator1 = 0.0;
    double denominator2 = 0.0;
    for (int i = 0; i < size; i++) {
        numerator += (vector1[i] - mean1) * (vector2[i] - mean2);
        denominator1 += pow(vector1[i] - mean1, 2.0);
        denominator2 += pow(vector2[i] - mean2, 2.0);
    }
    double denominator = sqrt(denominator1 * denominator2);
    return numerator / denominator;
}

int main() {
    const int size = 10; // 向量长度
    double reference[size] = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0}; // 参考向量
    double test1[size] = {1.1, 2.2, 2.9, 3.7, 4.8, 5.9, 6.8, 7.9, 8.7, 9.6}; // 测试向量1
    double test2[size] = {0.9, 2.1, 3.1, 4.2, 5.1, 6.2, 7.2, 8.1, 9.5, 10.2}; // 测试向量2
    double mean1, mean2; // 参考向量和测试向量的几何平均值
    geometricMean(reference, size, &mean1);
    geometricMean(test1, size, &mean2);
    // 计算测试向量1与参考向量的夹角余弦值和相关系数
    double cosine1 = cosineSimilarity(reference, test1, size);
    double correlation1 = correlationCoefficient(reference, test1, size);
    cout << "cosine similarity between reference and test1: " << cosine1 << endl;
    cout << "correlation coefficient between reference and test1: " << correlation1 << endl;
    // 计算测试向量2与参考向量的夹角余弦值和相关系数
    double cosine2 = cosineSimilarity(reference, test2, size);
    double correlation2 = correlationCoefficient(reference, test2, size);
    cout << "cosine similarity between reference and test2: " << cosine2 << endl;
    cout << "correlation coefficient between reference and test2: " << correlation2 << endl;
    // 比较夹角余弦值和相关系数，根据不同的阈值进行分类
    double threshold1 = 0.95;
    double threshold2 = 0.9;
    if (cosine1 > threshold1 && correlation1 > threshold2) {
        cout << "test1 belongs to the same class as reference" << endl;
    } else {
        cout << "test1 belongs to a different class from reference" << endl;
    }
    if (cosine2 > threshold1 && correlation2 > threshold2) {
        cout << "test2 belongs to the same class as reference" << endl;
    } else {
        cout << "test2 belongs to a different class from reference" << endl;
    }
    return 0;
}

该代码中使用了几个函数来计算向量的几何平均值、欧几里得范数、夹角余弦值和相关系数。首先，计算参考向量和测试向量的几何平均值，然后分别计算测试向量1和测试向量2与参考向量的夹角余弦值和相关系数。最后，根据不同的阈值进行分类，输出测试向量1和测试向量2属于哪个类别。