ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2014, Vol. 51 ›› Issue (9): 1911-1918.doi: 10.7544/issn1000-1239.2014.20140199

Special Issue: 2014深度学习

Previous Articles     Next Articles

Remote Sensing Image Classification Based on DBN Model

Lü Qi1, Dou Yong1, Niu Xin1, Xu Jiaqing1, Xia Fei2   

  1. 1(National Laboratory for Parallel and Distributed Processing, School of Computer, National University of Defense Technology, Changsha 410073);2(Electronic Engineering College, Naval University of Engineering, Wuhan 430033)
  • Online:2014-09-01

Abstract: Remote sensing image classification is one of the key technologies in geographic information system (GIS), and it plays an important role in modern urban planning and management. In the field of machine learning, deep learning is springing up in recent years. By mimicking the hierarchical structure of human brain, deep learning can extract features from lower level to higher level gradually, and distill the spatio-temporal regularizes of input data, thus improve the classification performance. Deep belief network (DBN) is a widely investigated and deployed deep learning model. It combines the advantages of unsupervised and supervised learning, and can archive good classification performance for high-dimensional data. In this paper, a remote sensing image classification method based on DBN model is proposed. This is one of the first attempts to apply deep learning approach to urban detailed classification. Six-day high-resolution RADARSAT-2 polarimetric synthetic aperture radar (SAR) data were used for evaluation. Experimental results show that the proposed method can outperform SVM (support vector machine) and traditional neural network (NN).

Key words: remote sensing image, synthetic aperture radar(SAR), land cover classification, deep learning, restricted Boltzmann machine (RBM), deep belief network (DBN)

CLC Number: