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access icon openaccess Scene classification of remote sensing images based on hierarchical sparse coding

  • Author(s): Xu Jiaqing 1 ; Lv Qi 2 ; Liu Hongjun 3 ; He Jie 1
    • View affiliations
  • Source: Volume 2018, Issue 16, November 2018, p. 1650 – 1657
    DOI:  10.1049/joe.2018.8268 , Online ISSN 2051-3305
This is an open access article published by the IET under the Creative Commons Attribution -NonCommercial License (http://creativecommons.org/licenses/by-nc/3.0/)
Received 17/07/2018, Accepted 26/07/2018, Published 16/08/2018

Remote sensing image scene classification is an important method for remote sensing image analysis and interpretation and plays an important role in civil and military fields. In this study, a scene classification method of remote sensing images based on hierarchical sparse coding is proposed. This method is essentially a kind of multi-layer, multi-scale, and multi-path sparse coding. It can extract features of optical remote sensing images more effectively, so that the features of the remote sensing images can be represented more sufficiently. The obtained codes are further used for spatial pyramid pooling (SPP) operation, and the corresponding SPP representation is obtained. SPP representations in different paths are combined and outputted to the support vector machine classifier, and the final classification results are obtained. Experiments on two data sets show that the proposed method can obtain better scene classification accuracy.

Inspec keywords: image classification; feature extraction; geophysical image processing; support vector machines; object detection; remote sensing

Other keywords: scene classification accuracy; optical remote sensing images; hierarchical sparse coding; sensing image scene classification; remote sensing image analysis; scene classification method; multipath sparse coding

Subjects: Computer vision and image processing techniques; Optical, image and video signal processing; Knowledge engineering techniques

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