The adaptive Gaussian mixture model is a probability model that can be used to represent the probability distribution of K in the overall distribution. The mixed model represents the probability distribution of the overall observed data. This is a mixed distribution composed of K sub-distributions. In the mixed model, in order to calculate the probability of the observation data in the overall distribution, the observation data is not required to provide information about the sub-distribution. The EM algorithm is used to estimate the parameters of a probability model with hidden variables. Large-scale analysis of remote sensing images of various temporal, climate, and terrain types of mountain environmental characteristics is one of the most important issues at present. In this experiment, 6 Landsat TM remote sensing images with different longitudes and latitudes, different land use patterns, different realities, different ranges, different terrains, and different climates were selected as the research objects. Through their comprehensive comparison, the general ones were selected. The supervised classification method (most likelihood method, BP neural network and support vector machine method) classifies Landsat TM remote sensing images. In order to improve the accuracy of remote sensing image classification and the accuracy of land use information extraction, data such as normalized vegetation index and texture features are used to classify the experimental samples. Cluster statistics and filter analysis are used to classify the results. Finally, a confusion matrix is used to evaluate the accuracy of the classification results.

自适应高斯混合模型是一种概率模型，可以用来表示K在整体分布中的概率分布。混合模型表示整体观测数据的概率分布。这是一个由K组成的混合分布子分布。在混合模型中，为了计算观测数据在整体分布中的概率，观测数据不需要提供子分布的信息。EM 算法用于估计具有隐藏变量的概率模型的参数。对山地环境特征的各种时间、气候和地形类型的遥感图像进行大规模分析是当前最重要的问题之一。本实验选取了6幅不同经纬度、不同土地利用方式、不同现实、不同范围、不同地形、不同气候的Landsat TM遥感影像作为研究对象。通过他们的综合比较，选择了一般的。监督分类方法（最大似然法、BP 神经网络和支持向量机方法）对 Landsat TM 遥感影像进行分类。为提高遥感影像分类精度和土地利用信息提取精度，利用归一化植被指数、纹理特征等数据对实验样本进行分类。聚类统计和过滤分析用于对结果进行分类。最后，使用混淆矩阵来评估分类结果的准确性。归一化植被指数和纹理特征等数据用于对实验样本进行分类。聚类统计和过滤分析用于对结果进行分类。最后，使用混淆矩阵来评估分类结果的准确性。归一化植被指数和纹理特征等数据用于对实验样本进行分类。聚类统计和过滤分析用于对结果进行分类。最后，使用混淆矩阵来评估分类结果的准确性。