Matches in SemOpenAlex for { <https://semopenalex.org/work/W4283784434> ?p ?o ?g. }
- W4283784434 endingPage "955" @default.
- W4283784434 startingPage "955" @default.
- W4283784434 abstract "This paper presents an evaluation of land cover accuracy, particularly regarding oil palm crop cover, using optical/synthetic aperture radar (SAR) image fusion methods through the implementation of the random forest (RF) algorithm on cloud computing platforms using Sentinel-1 SAR and Sentinel-2 optical images. Among the fusion methods evaluated were Brovey (BR), high-frequency modulation (HFM), Gram–Schmidt (GS), and principal components (PC). This work was developed using a cloud computing environment employing R and Python for statistical analysis. It was found that an optical/SAR image stack resulted in the best overall accuracy with 82.14%, which was 11.66% higher than that of the SAR image, and 7.85% higher than that of the optical image. The high-frequency modulation (HFM) and Brovey (BR) image fusion methods showed overall accuracies higher than the Sentinel-2 optical image classification by 3.8% and 3.09%, respectively. This demonstrates the potential of integrating optical imagery with Sentinel SAR imagery to increase land cover classification accuracy. On the other hand, the SAR images obtained very high accuracy results in classifying oil palm crops and forests, reaching 94.29% and 90%, respectively. This demonstrates the ability of synthetic aperture radar (SAR) to provide more information when fused with an optical image to improve land cover classification." @default.
- W4283784434 created "2022-07-04" @default.
- W4283784434 creator A5004377595 @default.
- W4283784434 creator A5050063103 @default.
- W4283784434 creator A5068199850 @default.
- W4283784434 date "2022-07-01" @default.
- W4283784434 modified "2023-10-05" @default.
- W4283784434 title "Evaluation of SAR and Optical Image Fusion Methods in Oil Palm Crop Cover Classification Using the Random Forest Algorithm" @default.
- W4283784434 cites W1210292870 @default.
- W4283784434 cites W1969214006 @default.
- W4283784434 cites W1996777760 @default.
- W4283784434 cites W1998183356 @default.
- W4283784434 cites W2014158985 @default.
- W4283784434 cites W2019185548 @default.
- W4283784434 cites W2030786150 @default.
- W4283784434 cites W2044465660 @default.
- W4283784434 cites W2046328718 @default.
- W4283784434 cites W2046888627 @default.
- W4283784434 cites W2049759394 @default.
- W4283784434 cites W2080562691 @default.
- W4283784434 cites W2112206513 @default.
- W4283784434 cites W2158537567 @default.
- W4283784434 cites W2170888285 @default.
- W4283784434 cites W2172922818 @default.
- W4283784434 cites W2296297379 @default.
- W4283784434 cites W2317983151 @default.
- W4283784434 cites W2567262610 @default.
- W4283784434 cites W2617645388 @default.
- W4283784434 cites W2729921439 @default.
- W4283784434 cites W2734691593 @default.
- W4283784434 cites W2776146695 @default.
- W4283784434 cites W2792431031 @default.
- W4283784434 cites W2809370310 @default.
- W4283784434 cites W2885221142 @default.
- W4283784434 cites W2885910915 @default.
- W4283784434 cites W2895854890 @default.
- W4283784434 cites W2896311823 @default.
- W4283784434 cites W2900295757 @default.
- W4283784434 cites W2901019616 @default.
- W4283784434 cites W2911964244 @default.
- W4283784434 cites W2912140217 @default.
- W4283784434 cites W2937019780 @default.
- W4283784434 cites W2941722341 @default.
- W4283784434 cites W2946413603 @default.
- W4283784434 cites W2968510217 @default.
- W4283784434 cites W2974816519 @default.
- W4283784434 cites W2999000103 @default.
- W4283784434 cites W2999010101 @default.
- W4283784434 cites W3008910742 @default.
- W4283784434 cites W3016087557 @default.
- W4283784434 cites W3082077183 @default.
- W4283784434 cites W3095757848 @default.
- W4283784434 cites W3096462768 @default.
- W4283784434 cites W3104356922 @default.
- W4283784434 cites W3107669251 @default.
- W4283784434 cites W3108730420 @default.
- W4283784434 cites W3112727310 @default.
- W4283784434 cites W3118969018 @default.
- W4283784434 cites W3134130181 @default.
- W4283784434 cites W3135401024 @default.
- W4283784434 cites W3150957317 @default.
- W4283784434 cites W3163231391 @default.
- W4283784434 cites W3164067898 @default.
- W4283784434 doi "https://doi.org/10.3390/agriculture12070955" @default.
- W4283784434 hasPublicationYear "2022" @default.
- W4283784434 type Work @default.
- W4283784434 citedByCount "8" @default.
- W4283784434 countsByYear W42837844342022 @default.
- W4283784434 countsByYear W42837844342023 @default.
- W4283784434 crossrefType "journal-article" @default.
- W4283784434 hasAuthorship W4283784434A5004377595 @default.
- W4283784434 hasAuthorship W4283784434A5050063103 @default.
- W4283784434 hasAuthorship W4283784434A5068199850 @default.
- W4283784434 hasBestOaLocation W42837844341 @default.
- W4283784434 hasConcept C111919701 @default.
- W4283784434 hasConcept C115961682 @default.
- W4283784434 hasConcept C127313418 @default.
- W4283784434 hasConcept C127413603 @default.
- W4283784434 hasConcept C147176958 @default.
- W4283784434 hasConcept C154945302 @default.
- W4283784434 hasConcept C169258074 @default.
- W4283784434 hasConcept C206887242 @default.
- W4283784434 hasConcept C2780648208 @default.
- W4283784434 hasConcept C39432304 @default.
- W4283784434 hasConcept C41008148 @default.
- W4283784434 hasConcept C4792198 @default.
- W4283784434 hasConcept C62649853 @default.
- W4283784434 hasConcept C69744172 @default.
- W4283784434 hasConcept C75294576 @default.
- W4283784434 hasConcept C79974875 @default.
- W4283784434 hasConcept C87360688 @default.
- W4283784434 hasConceptScore W4283784434C111919701 @default.
- W4283784434 hasConceptScore W4283784434C115961682 @default.
- W4283784434 hasConceptScore W4283784434C127313418 @default.
- W4283784434 hasConceptScore W4283784434C127413603 @default.
- W4283784434 hasConceptScore W4283784434C147176958 @default.
- W4283784434 hasConceptScore W4283784434C154945302 @default.
- W4283784434 hasConceptScore W4283784434C169258074 @default.