Abstract

The main objective of this study was to improve the long-term land use change detection by improving classification accuracy of previous generation satellite image using a recent super-resolution technique. The study also analysed the change in land cover over a period of 41 years in a coal mining area. A dual-tree complex wavelet transform-based image super-resolution technique was used to enhance Landsat images of 1975 and 2016. Separating pixels with similar spectral response is an enigmatical task, especially when those pixel represent different ground features. Therefore, an advanced neural net supervised classifier was used to minimize classification errors. Accuracy of the classified images (both super-resolved and original) were measured using confusion matrices and kappa coefficients. A significant improvement of more than 10% was observed in the overall classification accuracy for the image of 1975, highlighting that the classification accuracy of earlier generation satellite data can be improved substantially.

Keywords

Image enhancement; complex wavelet transform; artificial neural network; change detection; coal mining

Published in

Geocarto International
2018, Volume: 33, number: 10, pages: 1084-1094
Publisher: Informa {UK} Limited

UKÄ Subject classification

Remote Sensing


Publication identifier

DOI: https://doi.org/10.1080/10106049.2017.1333534

Permanent link to this page (URI)

https://res.slu.se/id/publ/114840