1
Department of Computer Science and Engineering, Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India
(thyla.csrd@gmail.com)
2
Department of Computer Science and Engineering, Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India
(researchwork411@gmail.com)
Abstract :
Constraints perceived in different socioeconomic situations reinforce land use patterns and land cover (LULC) at different levels. However, the statistical information regarding the LULC variations encounters enormous significance for the execution and modelling of appropriate environmental variations and resource management with the available remote sensed data from diverse satellite images and advanced computing technologies; information is generally retrieved from the image classification approaches. However, a broader quantitative analysis of various classification approaches is crucial to choosing an effectual classifier model to acquire appropriate land use regions. We concentrate on the Karavetti region and its related fields in this study. We use a Non-Linear Recurrent Convolutional Neural Network (NLR-CNN) to analyze the data statistically. Well-known techniques such as Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT), among others are used to evaluate the model performance. High-resolution images and the data points supplied are also used to assess the accuracy of the categorization and prediction. A confusion matrix is generated where the land cover regions show superior classification accuracy with the fusion model. Also, the NDVI facts and additional metrics like loss, error rate and kappa coefficients are analyzed. Therefore, the outcomes show that the anticipated is considered more robust with better performance to enhance the classification accuracy with the specific land cover regions.
Keywords :
land cover; land use; classification; learning approaches; and fusion-based prediction
References :
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| Style | # |
|---|---|
| MLA | Thylashri S., Rajalakshmi N. R.. "Wetland Mapping by Fusion of Deep learning and Ensemble Model for Enhancing Prediction Outcomes." Fusion: Practice and Applications, Vol. 14, No. 1, 2024 ,PP. 178-189 (Doi : https://doi.org/10.54216/FPA.140115) |
| APA | Thylashri S., Rajalakshmi N. R.. (2024). Wetland Mapping by Fusion of Deep learning and Ensemble Model for Enhancing Prediction Outcomes. Journal of Fusion: Practice and Applications, 14 ( 1 ), 178-189 (Doi : https://doi.org/10.54216/FPA.140115) |
| Chicago | Thylashri S., Rajalakshmi N. R.. "Wetland Mapping by Fusion of Deep learning and Ensemble Model for Enhancing Prediction Outcomes." Journal of Fusion: Practice and Applications, 14 no. 1 (2024): 178-189 (Doi : https://doi.org/10.54216/FPA.140115) |
| Harvard | Thylashri S., Rajalakshmi N. R.. (2024). Wetland Mapping by Fusion of Deep learning and Ensemble Model for Enhancing Prediction Outcomes. Journal of Fusion: Practice and Applications, 14 ( 1 ), 178-189 (Doi : https://doi.org/10.54216/FPA.140115) |
| Vancouver | Thylashri S., Rajalakshmi N. R.. Wetland Mapping by Fusion of Deep learning and Ensemble Model for Enhancing Prediction Outcomes. Journal of Fusion: Practice and Applications, (2024); 14 ( 1 ): 178-189 (Doi : https://doi.org/10.54216/FPA.140115) |
| IEEE | Thylashri S., Rajalakshmi N. R., Wetland Mapping by Fusion of Deep learning and Ensemble Model for Enhancing Prediction Outcomes, Journal of Fusion: Practice and Applications, Vol. 14 , No. 1 , (2024) : 178-189 (Doi : https://doi.org/10.54216/FPA.140115) |