Developing a general post-classification framework for land-cover mapping improvement using high-spatial-resolution remote sensing imagery

ABSTRACT

In this letter, a general post-classification framework (GPCF) is proposed to enhance initial results. Traditional post-classification techniques usually improve classification accuracy by considering the contextual information in a single classified image. In contrast to traditional techniques, the proposed GPCF aims to integrate multi-source classified images obtained through different classification approaches. In the proposed framework, the label of a central pixel is determined by its surrounding voting in each classified image. In this manner, the GPCF can integrate the advantages of different classification approaches. In our experiments, a hyperspectral image and an aerial image with high spatial resolution (HSR) are used to evaluate the proposed GPCF. Compared with two relevant post-classification approaches, the proposed framework can provide a land-cover map with lower noise in visual comparison and achieve higher classification accuracies. Therefore, the proposed GPCF presents better performance in HSR image classification.

Acknowledgements

The authors would like to thank the editors and reviewers. This work was supported by Key Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and Geoinformation (2015NGCM), the project from the China Postdoctoral Science Foundation (2015M572658XB).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Key Laboratory for National Geographic Census and Monitoring，National Administration of Surveying, Mapping and Geoinformation [2015NGCM];the China Postdoctoral Science Foundation [2015M572658XB]; National Natural Science Foundation of China (61472319).​​​​