Matches in SemOpenAlex for { <https://semopenalex.org/work/W3186140930> ?p ?o ?g. }
- W3186140930 endingPage "416" @default.
- W3186140930 startingPage "398" @default.
- W3186140930 abstract "The increased open-access availability of radar and optical satellite imagery has engendered numerous land use and land cover (LULC) analyses combining these data sources. In parallel, cloud computing platforms have enabled a wider community to perform LULC classifications over long periods and large areas. However, an assessment of how the performance of classifiers available on these cloud platforms can be optimized for the use of multi-imagery data has been lacking for multi-temporal LULC approaches. This study provides such an assessment for the supervised classifiers available on the open-access Google Earth Engine platform: Naïve Bayes (NB), Classification and Regression Trees (CART), Random Forest (RF), Gradient Tree Boosting (GTB), and Support Vector Machines (SVM). A multi-temporal LULC analysis using Sentinel-1 and 2 is implemented for a study area in the Mekong Delta. Classifier performance is compared for different combinations of input imagery, band sets, and training datasets. The results show that GTB and RF yield the highest overall accuracies, at 94% and 93%. Combining optical and radar imagery boosts classification accuracy for CART, RF, GTB, and SVM by 10–15 percentage points. Furthermore, it reduces the impact of limited training dataset quality for RF, GTB, and SVM." @default.
- W3186140930 created "2021-08-02" @default.
- W3186140930 creator A5000785881 @default.
- W3186140930 creator A5003503242 @default.
- W3186140930 creator A5010057631 @default.
- W3186140930 creator A5046827728 @default.
- W3186140930 creator A5058676173 @default.
- W3186140930 date "2021-01-01" @default.
- W3186140930 modified "2023-10-18" @default.
- W3186140930 title "Input imagery, classifiers, and cloud computing: Insights from multi-temporal LULC mapping in the Cambodian Mekong Delta" @default.
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