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Refining Training Samples Using Median Absolute Deviation for Supervised Classification of Remote Sensing Images

  • Xunqiang Gong
  • Li ShenEmail author
  • Tieding Lu
Research Article
  • 44 Downloads

Abstract

Supervised image classification refers to the task of extracting information classes from a multi-band remote sensing image. The selection of training samples is critical and directly influences supervised classification accuracy. However, some impure training samples are possible selected because of human mistakes or limited labeling conditions, which leads to a reduction in the classification accuracy. To solve this issue, median absolute deviation (MAD) is adopted to refine training samples. A comparison of the full and refined training samples is conducted for the same classifier, i.e., maximum likelihood classification (MLC) or support vector machine (SVM), through experimental evaluation with two sets of experiments. The results of experiments show that the overall accuracy and the kappa coefficient of the refined training samples significantly outperform those of the full training samples for the same classifier (MLC or SVM). It shows that refining training samples using the MAD can effectively eliminate the influence of impure training samples so that the more reliable and accurate results can be obtained.

Keywords

Supervised image classification Refining training samples Outlier detection Median absolute deviation 

Notes

Acknowledgments

This work was jointly supported by the National Key Research and Development Plan of China [Grand 2016YFB0501403; Grand 2016YFB0501405], the Doctoral Scientific Research Foundation of East China University of Technology [DHBK2017158] and the Key Laboratory for Digital Land and Resources of Jiangxi Province [DLLJ201808]. The first author was financially supported by the China Scholarship Council (CSC) for his study at the Technical University of Berlin, Germany, with Prof. Frank Neitzel.

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Copyright information

© Indian Society of Remote Sensing 2018

Authors and Affiliations

  1. 1.Faculty of GeomaticsEast China University of TechnologyNanchangPeople’s Republic of China
  2. 2.State-Province Joint Engineering Laboratory of Spatial Information Technology for High-Speed Railway SafetySouthwest Jiaotong UniversityChengduPeople’s Republic of China

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