Remote sensing image classification based on BP neural network model
(1.Shandong University of Science and Technology, Taian 271019, China2.Chinese Academy of Surveying and Mapping, Beijing 100039, China3.Shandong University of Science and Technology, Tai’an 271019, China)
Abstract: Aiming at the characteristics of remote sensing image classification, the mixed pixel problem is one of the main factors that affect the improvement of classifying precision in remote sensing classification. A BP neural network was established to solve mixed pixel classifying problems. The aim of our work is to improve the BP network algorithm and set the intensity of training, which changes with training process, because the BP algorithm converyging speed of learning algorithm is rather slow, it is possible to fall into the local minimum, and because the algorithm makes the learning result poor, the global minimum value can’t be reached. The results show that this method effectively solves mixed pixel classifying problem, improves learning speed and classification accuracy of BP network classifier,so it is one kind of effective remote sensing imagery classifying method.
Key words: remote sensing image； classification； neural network； training intensity