The Building Recognition and Analysis of Remote Sensing Image Based on Depth Belief Network

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.