An Innovative Fusion-Based Scenario for Improving Land Crop Mapping Accuracy.

ABSTRACT: The accuracy of land crop maps obtained from satellite images depends on the type of feature selection algorithm and classifier. Each of these algorithms have different efficiency in different conditions; therefore, developing a suitable strategy for combining the capabilities of different algorithms in preparing a land crop map with higher accuracy can be very useful. The objective of this study was to develop a fusion-based framework for improving land crop mapping accuracy. First, the features were retrieved using the Sentinel 1, Sentinel 2, and Landsat-8 imagery. Then, training data and various feature selection algorithms including recursive feature elimination (RFE), random forest (RF), and Boruta were used for optimal feature selection. Various classifiers, including artificial neur

SUBMITTER: Fathololoumi S 

PROVIDER: S-EPMC9571136 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature