The building recognition and analysis of remote sensing image based on depth belief network |
| |
| Affiliation: | 1. School of Computer Science, China University of Geosciences, Wuhan 430074, China;2. Network and Information Center, China University of Geosciences, Wuhan 430074, China;1. Information Technology, Faculty of Information Technology and Communication Sciences, Tampere University, P.O. Box 1001, 30014 Tampere, Finland;2. Mathematics and Statistics, Faculty of Information Technology and Communication Sciences, Tampere University, P.O. Box 1001, 30014 Tampere, Finland;1. College of Mathematics and Statistics, Hengyang Normal University, Henyang 421001, China;2. College of Computer Science, Sichuan University, Chengdu 610065, China;3. School of Computer Science and Engineering, Beihang University, Beijing 100191, China;4. College of Science, China University of Petroleum, Qingdao 266580, China;1. Russian State University for the Humanities, Moscow, Russia;2. National Research Center “Kurchatov Institute”, Moscow, Russia;3. National Research Nuclear University MEPhI, Moscow, Russia;4. Institute for Advanced Brain Studies, Lomonosov Moscow State University, Moscow, Russia;5. Mental-Health Clinic No. 1 Named after N.A. Alexeev, Moscow, Russia |
| |
| Abstract: | The deep belief network model, which is widely used in deep learning, consists of a multi-layer constrained Boltzmann machine and a back-propagation network. The authors have conducted parameter sensitivity experiments on the number of iterations, the number of hidden layers and the number of hidden layer nodes in the DBN network for remote sensing image classification, and obtained a set of optimal parameter setting schemes. Moreover, the DBN algorithm has been enhanced with an improved Dropout strategy. The improved Dropout strategy selects only part of the data to clear the weight at a time, and a local area randomly clear strategy is adopted, which will save the local information of the image itself, and enhance the generalization ability of the model. In order to verify the advantages of the improved DBN algorithm model, the classification results of DBN, KNN, random forest and SVM have been compared. And the results show that classification accuracy of the improved DBN has been greatly improved, which is increased by about 2.5% compared to DBN. The improved DBN classification results are processed then, including connected areas marking, noise removal, morphological transformation and edge extraction, and the boundary information of the building is obtained according to the target shape characteristics. Finally, the experiment on the morphological characteristics of the building also shows it can extract better edge information of the building. |
| |
| Keywords: | Remote sensing image DBN Dropout strategy Classification Building |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
