Improving Gram–Schmidt Adaptive Pansharpening Method Using Support Vector Regression and Markov Random Field

This study aimed to propose an improved Gram–Schmidt adaptive (GSA) pansharpening method using the support vector regression (SVR) and Markov random field (MRF) models in the cases of high ratios between spatial resolutions of LRMS and PAN images. In the present study, the SVR model was used to mode...

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Veröffentlicht in:	Journal of the Indian Society of Remote Sensing 2024-09, Vol.52 (9), p.2073-2081
Hauptverfasser:	Choe, Won-Il, Jo, Jong-Song, Ri, Kum-Su, Sok, Kwang-Chol, Ri, Yong-Ryong
Format:	Artikel
Sprache:	eng
Schlagworte:	Earth and Environmental Science Earth Sciences Fields (mathematics) Image quality Remote Sensing/Photogrammetry Research Article Support vector machines
Online-Zugang:	Volltext
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