Abstract: In view of the shortcomings of the low accuracy and slow speed of current remote sensing image classification algorithm, we proposed a remote sensing image classification algorithm based on quantum particle swarm optimization to improve the classification effect of remote sensing images. Firstly, we analyzed the shortcomings of the current remote sensing image classification algorithm and its reasons. Secondly, we extracted the original features of various types of remote sensing images, and used the quantum particle swarm algorithm to select features and extract important features of remote sensing image classification results. Finally, using least squares support vector machine, we established classifier for remote sensing image, realized remote sensing image classification and recognition, and carried out simulation and contrast experiment of remote sensing image classification. The experimental results show that the proposed algorithm overcomes the limitations of current remote sensing image classification algorithms, and the classification accuracy of remote sensing image has been greatly improved, which effectively reduces the error of image classification and improves the efficiency of image classification.

Key words: feature extraction, remote sensing technology, particle swarm optimization algorithm, quantum behavior, classifier design