Research on Remote Sensing Image Terrain Classification Algorithm Based on Improved KNN

Abstract

In recent years, with the development of machine learning technology, neural networks have gradually become a convenient method for classification of remote sensing image features. This article briefly describes the structure and principle of the process of remote sensing image feature recognition, using three remote sensing image data sets AID, NWPU-RESISC45, UC Merced Land Use dataset for algorithm testing. First, the AlexNet neural network is used to extract the remote sensing image features, and the KNN is used to achieve image classification. The effects of extracting different alexnet feature layers on the average classification accuracy on the three data sets are compared. This paper compares the advantages of KNN in terms of time through PCA dimensionality reduction and k-means clustering optimization before classification, at the end of the article, it summarizes and briefly describes the development trend of neural network in the application of remote sensing image features classification technology.