| Autor: |
Pramunendar, Ricardus Anggi, Ratmana, Danny Oka, Rafrastara, Fauzi Adi, Shidik, Guruh Fajar, Andono, Pulung Nurtantio, Sari, Yuslena, Evanita |
| Předmět: |
|
| Zdroj: |
International Journal of Intelligent Engineering & Systems; 2024, Vol. 17 Issue 6, p139-151, 13p |
| Abstrakt: |
Forest cover in Indonesia reached 95.97 million hectares or 51.2% of the total land area. Fluctuations in forest cover from 2014 to 2022 highlight the need for accurate land mapping to understand factors influencing landscape patterns. Decreases in forest cover increase vulnerability to natural disasters, particularly floods, causing significant economic and environmental losses. This study focuses on strategies for land cover mapping, especially in under-monitored regions, leveraging recent advancements in satellite and drone data for rapid and precise mapping. Deep learning methods like convolutional neural networks (CNN) have shown high accuracy in vegetation cover detection, which can be further improved with new activation functions. This study proposes the new modification activation function, with adding a linear component to enhance CNN accuracy and effectiveness. The proposed demonstrated a significant increase in accuracy, maintaining high values during testing (90.07%) on the Vegetation Density of Peatland Drone dataset. Statistical analysis showed high data distribution with significant performance variation. Despite slower computation times, the proposed excelled in convergence speed and effectiveness compared to other activation. ANOVA and Friedman analyses confirmed significant performance and computation time differences among activation methods. The proposed shows excellent potential in enhancing CNN model accuracy and stability for land cover classification. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
