Reducing the impacts of intra-class spectral variability on the accuracy of soft classification and super-resolution mapping of shoreline

Doan, Huong T.X.; Foody, Giles M.; Bui, Dieu Tien

Abstract

The main objective of this research is to assess the impact of intra-class spectral variation on the accuracy of soft classification and super-resolution mapping. The accuracy of both analyses was negatively related to the degree of intra-class spectral variation, but the effect could be reduced through use of spectral sub-classes. The latter is illustrated in mapping the shoreline at a sub-pixel scale from Landsat ETM+ data. Reducing the degree of intra-class spectral variation increased the accuracy of soft classification, with the correlation between predicted and actual class coverage rising from 0.87 to 0.94, and super-resolution mapping, with the RMSE in shoreline location decreasing from 41.13 m to 35.22 m.

Citation

Doan, H. T., Foody, G. M., & Bui, D. T. (2019). Reducing the impacts of intra-class spectral variability on the accuracy of soft classification and super-resolution mapping of shoreline. International Journal of Remote Sensing, 40(9), 3384-3400. https://doi.org/10.1080/01431161.2018.1545099

• Journal Article Type: Article
• Acceptance Date: Oct 5, 2018
• Online Publication Date: Nov 13, 2018
• Publication Date: 2019
• Deposit Date: Nov 5, 2018
• Publicly Available Date: Nov 14, 2019
• Journal: International Journal of Remote Sensing
• Print ISSN: 0143-1161
• Electronic ISSN: 1366-5901
• Publisher: Taylor and Francis
• Peer Reviewed: Peer Reviewed
• Volume: 40
• Issue: 9
• Pages: 3384-3400
• DOI: https://doi.org/10.1080/01431161.2018.1545099
• Keywords: Intra-class spectral variability; Soft classification; Super-resolution mapping; Hopfield Neural Network; Contouring Based method; Shoreline mapping
• Public URL: https://nottingham-repository.worktribe.com/output/1223412
• Publisher URL: https://www.tandfonline.com/doi/full/10.1080/01431161.2018.1545099
• Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Remote Sensing on 13/11/2018, available online: http://www.tandfonline.com10.1080/01431161.2018.1545099
• Contract Date: Nov 5, 2018