Application of remote sensing image classification based on adaptive Gaussian mixture model in analysis of mountain environment features

Abstract

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.