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Articles by Helmi Zulhaidi Mohd Shafri
Total Records ( 3 ) for Helmi Zulhaidi Mohd Shafri
  Helmi Zulhaidi Mohd Shafri and Mohanad Saad Ezzat
  Problem statement: Oil palm trees are planted in large scale areas. The detection and mapping of diseases are considered as important for oil palm industry and need a timely detection to control the disease spread. Approach: Vegetation analysis of airborne hyperspectral imagery could be an ideal method to deal with this problem since this data could be acquired on user demand. Airborne hyperspectral dataset was preprocessed in order to prepare it for the vegetation analysis processing for the purpose of detection and mapping Ganoderma disease in oil palm trees. Many vegetation indices were tested and analyzed to classify oil palm trees into healthy and unhealthy trees, in both individual analysis of vegetation indices and forest health composites that are available in ENVI software. Accuracy assessment was calculated by using ground truth data. Results: The results showed that all vegetation indices tested in this study provide a good accuracy which ranges from 68.57-82.86 and 60-80% for both vegetation indices and forest health composites respectively. With regard to the vegetation indices the highest accuracy was achieved by using Red Edge Normalized Difference Vegetation Index (NDVI 705) with 82.86% of overall accuracy and as for the forest health composites the highest accuracy was achieved by using the composite that include Vogelmann Red Edge Index 1 (VOG1) with 80% of overall accuracy. Conclusion/Recommendations: Vegetation indices based on the red edge provide better results than other indices based on other techniques.
  Helmi Zulhaidi Mohd Shafri , Affendi Suhaili and Shattri Mansor
  Several classification algorithms for pattern recognition had been tested in the mapping of tropical forest cover using airborne hyperspectral data. Results from the use of Maximum Likelihood (ML), Spectral Angle Mapper (SAM), Artificial Neural Network (ANN) and Decision Tree (DT) classifiers were compared and evaluated. It was found that ML performed the best followed by ANN, DT and SAM with accuracies of 86%, 84%, 51% and 49% respectively.
  Helmi Zulhaidi Mohd Shafri and Redzuan Md Zeen
  Airborne hyperspectral remote sensing is a relatively new technology in Malaysia that needs to be tested for its feasibility. Various applications can benefit from the enormous potential offered such as in urban mapping in which rapid development in Malaysia can be accurately monitored. However, the use of hyperspectral data will also depend critically on the selection of suitable classifiers in order to extract the information. Hence, in this study, image classification was performed using various classifiers such as Parallelepiped, Minimum Distance, Mahalanobis Distance, Maximum Likelihood (ML), Spectral Information Divergence (SID), Spectral Angle Mapper (SAM), Binary Encoding (BE), Neural Network (NN) and Support Vector Machine (SVM). The accuracy of the classifiers was measured based on comparisons with ground truth data. SVM classifier shows the highest overall accuracy (87.98%) followed by ML with 83.17% and BE achieved the lowest accuracy with 39.28%. The findings indicate the feasibility of hyperspectral remote sensing for mapping urban environment in Malaysia with SVM as the most effective classifier for that purpose.
 
 
 
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