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Sparsity-regularized feature selection for multi-class remote sensing image classification

  • Tao Chen
  • Ye Zhao
  • Yanrong GuoEmail author
Multi-Source Data Understanding (MSDU)
  • 16 Downloads

Abstract

Remote sensing image classification plays an important role in a wide range of applications and has caused widely concerns. During the last few years, great efforts have been made to develop a number of scene classification methods for remote sensing images. However, the existing remote sensing image classification methods do not perform satisfactorily in dealing with multi-class classification problems and rely heavily on the quality of data sets. These disadvantages seriously restrict the application of remote sensing image, including industrial research, analysis and calculation of land use and land coverage. To this end, this paper proposes a remote sensing image classification algorithm based on the sparse regularized feature learning method. Specifically, after constructing bag of features by using speeded up robust features extraction algorithm, direct sparsity optimization-based feature selection method is applied for selecting discriminative features, which is used for constructing support vector machine classifier model. The proposed algorithm has been evaluated and compared with other advanced feature selection methods on four public remote sensing image data sets. The experimental results demonstrate the effectiveness of our proposed image classification algorithm, which has been successfully applied to remote sensing image classification tasks.

Keywords

Feature extraction Bag of features Sparsity-regularized feature selection Remote sensing image classification 

Notes

Acknowledgements

The research was supported by the National Key R&D Program of China under Grant No. 2017YFC0820604, Anhui Provincial Natural Science Foundation under Grant No. 1808085QF188, and National Nature Science Foundation of China under grant Nos. 61702156, 61502138, 61772171 and 61876056.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer and InformationHefei University of TechnologyHefeiChina

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