Change Detection of Remote Sensing Image Based on Deep Neural Networks

How to improve the quality of difference image (DI) for change detection task is an important issue in remote sensing images. This paper propose a new DI creation method based on deep neural networks. Deep belief network (DBN) which is an important model in deep learning is applied, and back propagation (BP) algorithm is improved according to change detection task in our method. The modified BP algorithm tunes DBN to increase the difference on changed areas and decrease the difference on unchanged areas in DI. The change detection result is obtained through clustering analysis of DI. The experiment result shows that the proposed method can avoid the radiometric correction procedures for change detection, enhance the difference in change areas remarkably, and suppress the noise effectively compared with traditional method of DI creation methods. The change detection result is more accurate, and the Kappa values increase 25.93% averagely. Keywords-back propagation; deep belief networks; remote sensing image; difference image; change detection

• Publication year

  2016

• Venue

  Unknown venue

• Publication date

  2016-11-20

• Fields of study

  Computer Science, Environmental Science

• Identifiers
  DOI 10.2991/AIIE-16.2016.61
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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