Information fusion techniques for change detection from multi-temporal remote sensing images

  • Authors:

  • Peijun Du;Sicong Liu;Junshi Xia;Yindi Zhao

  • Affiliations:

  • Dept. of Geographical Information Science, Nanjing University, Nanjing, China and Jiangsu Provincial Key Laboratory for Resources and Environment Information Engineering, China University of Minin ...;Dept. of Information Engineering and Computer Science, University of Trento, Trento, Italy;Jiangsu Provincial Key Laboratory for Resources and Environment Information Engineering, China University of Mining and Technology, Xuzhou, China;Jiangsu Provincial Key Laboratory for Resources and Environment Information Engineering, China University of Mining and Technology, Xuzhou, China

  • Venue:
  • Information Fusion
  • Year:
  • 2013

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Abstract

In order to investigate the impacts of different information fusion techniques on change detection, a sequential fusion strategy combining pan-sharpening with decision level fusion is introduced into change detection from multi-temporal remotely sensed images. Generally, change map from multi-temporal remote sensing images using any single method or single kind of data source may contain a number of omission/commission errors, degrading the detection accuracy to a great extent. To take advantage of the merits of multi-resolution image and multiple information fusion schemes, the proposed procedure consists of two steps: (1) change detection from pan-sharpened images, and (2) final change detection map generation by decision level fusion. Impacts of different fusion techniques on change detection results are evaluated by unsupervised similarity metric and supervised accuracy indices. Multi-temporal QuickBird and ALOS images are used for experiments. The experimental results demonstrate the positive impacts of different fusion strategies on change detection. Especially, pan-sharpening techniques improve spatial resolution and image quality, which effectively reduces the omission errors in change detection; and decision level fusion integrates the change maps from spatially enhanced fusion datasets and can well reduce the commission errors. Therefore, the overall accuracy of change detection can be increased step by step by the proposed sequential fusion framework.