2020
DOI: 10.3390/rs12223801
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A Novel Image Fusion Method of Multi-Spectral and SAR Images for Land Cover Classification

Abstract: The fusion of multi-spectral and synthetic aperture radar (SAR) images could retain the advantages of each data, hence benefiting accurate land cover classification. However, some current image fusion methods face the challenge of producing unexpected noise. To overcome the aforementioned problem, this paper proposes a novel fusion method based on weighted median filter and Gram–Schmidt transform. In the proposed method, Sentinel-2A images and GF-3 images are respectively subjected to different preprocessing p… Show more

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Cited by 44 publications
(24 citation statements)
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References 59 publications
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“…It is a non-parametric test based on a contingency table of 2 × 2 dimension constructed using the binary distinction between correct and incorrect class allocations, widely used to determine if statistically significant differences are present between pairs of classifications (Y. Gao et al, 2011);Millard & Richardson, 2015;Quan et al, 2020;Belgiu & Csillik, 2018;Kavzoglu, 2017;Dietterich, 1998;Sokolova et al, 2006). This test is suitable to assess the difference of multiple classification accuracy performances when the same set of validation and training samples are used (Foody, 2004;Y.…”
Section: Statistical Comparison Between Classification Results: Mcnemar's Testmentioning
confidence: 99%
“…It is a non-parametric test based on a contingency table of 2 × 2 dimension constructed using the binary distinction between correct and incorrect class allocations, widely used to determine if statistically significant differences are present between pairs of classifications (Y. Gao et al, 2011);Millard & Richardson, 2015;Quan et al, 2020;Belgiu & Csillik, 2018;Kavzoglu, 2017;Dietterich, 1998;Sokolova et al, 2006). This test is suitable to assess the difference of multiple classification accuracy performances when the same set of validation and training samples are used (Foody, 2004;Y.…”
Section: Statistical Comparison Between Classification Results: Mcnemar's Testmentioning
confidence: 99%
“…Random forest (RF) is a standard method of ensemble learning, with the outstanding output of classification and high processing speed, and it can prevent over-fitting effectively [51][52][53][54][55][56][57]. RF is a mixture of tree predictors.…”
Section: Random Forestmentioning
confidence: 99%
“…The parameters of the RTVSA method were set as follows: λ = 0.04, σ = 2, and L = 30. In image classification, according to the results in Reference [62] and our previous work [51], the number of decision trees in the RF was determined to be 100, and the number of prediction variables was set approximately to the square root of the number of input bands. The settings of the CNN were based on [59].…”
Section: Experiments Settingsmentioning
confidence: 99%
“…To analyze how entropy-based and CIs can complement each other, the combination of both types of indicators is studied. As the RF classifier provides excellent performance in pattern recognition [ 66 , 67 , 68 , 69 , 70 ], this study attempts to utilize the RF classifier to construct a classification model for fault recognition of low-speed bearings.…”
Section: Introductionmentioning
confidence: 99%