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Malay

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citcat translate

hafting

Last Update: 2014-04-11
Subject: General
Usage Frequency: 1
Quality:
Reference: Anonymous

citcat translate

derive

Last Update: 2014-04-11
Subject: General
Usage Frequency: 1
Quality:
Reference: Anonymous

Citcat translate

Di bawah adalah senarai nama untuk di rekodkan dalam sistem e-leave

Last Update: 2014-03-27
Subject: General
Usage Frequency: 1
Quality:
Reference: Anonymous

Citcat translate

awak

Last Update: 2014-03-23
Subject: General
Usage Frequency: 1
Quality:
Reference: Anonymous

Translation

Penterjemahan

Last Update: 2014-03-24
Usage Frequency: 26
Quality:
Reference: Wikipedia

Translation

bioremediation

Last Update: 2014-03-20
Usage Frequency: 3
Quality:
Reference: Wikipedia

Translation

select

Last Update: 2014-03-18
Usage Frequency: 2
Quality:
Reference: Wikipedia

Translation

Coral reef plays as the indicator of the reef health. However, these days’ coral had been severely facing the stress from human action and the global changes. For that it had to be studied and monitor gradually to preserve them for the future generation. It has been 40 years since remote sensing been used for coral mapping. However, they are no specific technique used for this mapping, Minimum Distances (Mindist), Artificial Neural Network (ANN), Maximum Likelihood (Maxlike) are the most common classifier for this type of mapping. This study was focused on Decision Tree Analysis (DTA) classifier that rarely used for coral habitat mapping. A decision tree is a structural mapping of binary decisions that lead to a decision about the class of an object. This paper focuses on the analysis of fine spatial resolution image to determined factors that contributes to coral mapping such as its texture, color, reflectance and so on. These factors were utilized as input into the decision tree analysis to generate coral distribution map. Using 2 types of DTA classification techniques which were Multivariate Decision Tree Analysis (MDTA) also Hybrid Decision Tree Analysis (HDTA) and two other techniques, Maxlike and Mindist. A comparison was done with the imagery before and after water column correction. It was to determine the effectiveness of this correction in increasing classification accuracies since there were claims, saying this correction are not benefiting much in term of classification accuracies. The results show hat for minimum distance before and after water column correction was done the overall accuracies were 39.52% and 54.49%, Maxlike; 48.5% and 60.48%; MDTA; 70.65% and 75.4% and finally the HDTA with the highest accuracies compare with other classifier with 80.23% before and 85.65% after the correction. This shows that the water column is still relevant in increasing classification accuracies. The poorest classifier for coral habitat mapping was Mindist and the best was HDTA. Compare with other classifiers HDTA can be incorporated with other classier and ancillary data such as texture analysis, distribution analysis as ancillary data in acquiring better classification results and its accuracy which can help in coral habitat monitoring in the future with its simple and easy methods.

Last Update: 2014-03-10
Usage Frequency: 4
Quality:
Reference: Wikipedia

Translation

whenever i am weary from the battles that rage in my head

Last Update: 2014-03-03
Usage Frequency: 4
Quality:
Reference: Wikipedia

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