Services on Demand
Journal
Article
Indicators
Cited by SciELO
Access statistics
Related links
Cited by Google
Similars in SciELO
Similars in Google
Share
Earth Sciences Research Journal
Print version ISSN 1794-6190
Abstract
ZHU, Danyao; WAN, Luhe and GAO, Wei. Fusion Methods Evaluation and Classification Suitability Study of Wetland Satellite Imagery. Earth Sci. Res. J. [online]. 2019, vol.23, n.4, pp.339-346. Epub Apr 20, 2020. ISSN 1794-6190. https://doi.org/10.15446/esrj.v23n4.84350.
Based on HJ-1A HSI data and Landsat-8 OLI data, RS image fusion experiments were carried out using three fusion methods: principal component (PC) transform, Gram Schimdt (GS) transform and nearest neighbor diffusion (NND) algorithm. Four evaluation indexes, namely mean, standard deviation, information entropy and average gradient, were selected to evaluate the fusion results from the aspects of image brightness, clarity and information content. Wetland vegetation was classified by spectral angle mapping (SAM) to find a suitable fusion method for wetland vegetation information extraction. The results show that PC fusion image contains the largest amount of information, GS fusion image has certain advantages in brightness and clarity maintenance, and NND fusion method can retain the spectral characteristics of the image to the maximum extent; among the three fusion methods, PC transform is the most suitable for wetland information extraction. It can retain more spectral information while improving spatial resolution, with classification accuracy of 89.24% and Kappa coefficient of 0.86.
Keywords : HJ-1A HIS; Landsat-8 OLI; Fusion method; Wetland classification.