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A Hybrid Method for Multi-sensor Remote Sensing Image Registration Based on Salience Region

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Abstract

In order to align the remote sensing images, we propose a novel hybrid method that combines image segmentation and salient region detection, which is inspired by human vision system. First of all, we present a novel superpixel-based method for dividing the image into sub-areas. Second, we propose a novel method based on color and image textures for detecting salient regions composed by superpixels. Then, we extract a new feature based on difference of Gaussian and local binary pattern from the salient regions. Finally, the sensed image is transformed by thin-plate spline. The proposed algorithm was tested on 30 pairs of remote sensing images and compared to other three state of the art methods. Experimental results show our approach is fast and robust, while still being efficient, which is better than other three methods.

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Notes

  1. http://www.cs.ubc.ca/~lowe/keypoints/.

  2. http://www.vision.ee.ethz.ch/~surf/download.html.

  3. http://www.vlfeat.org/~vedaldi/code/mser.html.

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Acknowledgments

The authors would like to express our sincere appreciation to the anonymous reviewers for their insightful comments, which have greatly aided us in improving the quality of the paper.

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Authors and Affiliations

  1. School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing , 100081, China

    Jichao Jiao & Zhongliang Deng

  2. Department of Engineering, Arizona State University—Polytechnic, Mesa, AZ , 85212, USA

    John Femiani

  3. School of Information Science and Technology, Beijing Institute of Technology, Beijing , 100081, China

    Baojun Zhao

  4. School of Astronautics, Northwestern Polytechnical University, Xi’an , 710072, China

    Xin Wang

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  1. Jichao Jiao
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  2. Zhongliang Deng
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  5. Xin Wang
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Correspondence to Jichao Jiao.

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Jiao, J., Deng, Z., Zhao, B. et al. A Hybrid Method for Multi-sensor Remote Sensing Image Registration Based on Salience Region. Circuits Syst Signal Process 33, 2293–2317 (2014). https://doi.org/10.1007/s00034-014-9763-z

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