Journal of Geo-information Science ›› 2016, Vol. 18 ›› Issue (5): 655-663.doi: 10.3724/SP.J.1047.2016.00655

• Orginal Article • Previous Articles     Next Articles

A Remote Sensing Change Detection Method Combining with Spatial-Spectral Features under the Guidance of Historical Interpretation Knowledge

WU Tianjun1,2,*(), MA Jianghong1   

  1. 1. Department of Mathematics and Information Science, College of Science, Chang'an University, Xi'an 710064
    2. Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province, Zhoushan 316022, China
  • Received:2015-12-15 Revised:2016-03-11 Online:2016-05-10 Published:2016-05-10
  • Contact: WU Tianjun E-mail:wutianjun1986@163.com

Abstract:

Remote sensing images with high-spatial-resolution can provide a great amount of detailed information for land resource monitoring, which makes the change detection using the high-spatial-resolution remote sensing images become the focus of current remote sensing research. This article proposes a remote sensing change detection method through combining with the spatial-spectral features under the guidance of historical interpretation knowledge. Firstly, the objects of consistent spatial position are constructed by segmenting the combined image from different phases. After that, the objects’ spectral and texture features are extracted. Then, we introduce the previous land cover maps to guide the DS evidence fusion for the similarities of two different types of features. The archived class-type label in each polygon from the land cover maps is used to direct the similarity fusion with different evidence confidences. Finally, a binarization method based on the Gaussian Mixture Model is adopted to extract the change regions. Experimental results show that this method can take advantage of the historical interpretation knowledge to guide the fusion of different object-feature-similarities; moreover, to a certain extent, to effectively improve the accuracy of change detection.

Key words: high-spatial-resolution remote sensing, object-based change detection, spatial-spectral features combination, DS Evidence Theory, Gaussian Mixture Model