Comparative Study of Machine Learning and Deep Learning in Remote Sensing Image Classification
Remote sensing image classification is an important part of the application of remote sensing technology. Machine learning and deep learning can achieve accurate, automated, rapid, definable, and scalable remote sensing image classification. This article compares four classification algorithms, namely machine learning algorithm support vector machine, deep learning algorithm convolu-tional neural network, deep confidence network, and stack based self coding network, and optimizes the parameters of support vector machine kernel function and the number of neurons in deep learning algorithm to achieve the highest classification accuracy. The ex-perimental results show that the overall classification accuracy and performance of the deep learning algorithm stack based self coding network are the highest, and it has good applicability and promotion value in remote sensing land classification in complex and diverse areas.
万方数据
维普
