Adaptive Ensemble Classification for Land Cover Classification Using Polarimetric Radar Images and Proposed Combination Rule

Document Type : Original Article

Authors

1 University of Birjand

2 دانشگاه بیرجند

Abstract

Due to numerous capabilities of polarimetric radar images as a source of strategic information, their use in military and commercial applications is rapidly growing. One of the important topics of interest to researchers is the classification of these images. So in this paper, the structure of an ensemble of classifiers based on sparse representation technique is presented which classifies adaptively two sets of border and non-border pixels in a polarimetric image. In this plan, various aspects such as base classifiers and their diversity, ensemble of classifier structure, reliability, combination rule, polarimetric and texture features and contextual information are considered. For designing the combination rule of base classifiers, a method based on reliability parameter is proposed. Also, a post-processing stage is used for reducing the unwanted discretion in the output of the ensemble of classifiers. Implementation of the proposed algorithms on a benchmark PolSAR image, demonstrates their superiority over that of traditional techniques.

Keywords

References

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History

  • Receive Date: 01 May 2018
  • Revise Date: 11 August 2019
  • Accept Date: 03 December 2018
  • Publish Date: 21 March 2018

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