Research on Classification of Remote Sensing Images Based on Artificial Intelligence

Abstract

With the rapid development of image processing technology, remote sensing technology has received increasing attention. Relying on artificial intelligence technology and using the advantages of principal component analysis (PCA) to reduce the dimensionality of features, this paper proposes a remote sensing image classification method based on SVM. First, LBP operator is used to extract remote sensing image features, and then PCA is used to perform remote sensing image features. The dimensionality reduction process reduces the feature dimensionality and eliminates feature redundant information, and obtains features that have a large contribution to the classification result. Finally, SVM is used for remote sensing image classification. The results show that PCA-SVM improves the efficiency and accuracy of remote sensing image classification.