Open AccessScene classification of remote sensing images based on hierarchical sparse coding
Author(s) -
Jiaqing Xu,
Qi Lv,
Hongjun Liu,
Jie He
Publication year - 2018
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2018.8268
Subject(s) - computer science , artificial intelligence , neural coding , pattern recognition (psychology) , sparse approximation , remote sensing , classifier (uml) , coding (social sciences) , support vector machine , pooling , contextual image classification , computer vision , pyramid (geometry) , image (mathematics) , geography , mathematics , statistics , geometry
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.