Coral reefs are one of the prominent marine ecosystems in the world. Coral reefs are facing threats from both natural and anthropogenic factors. Monitoring, protecting and studying these ecosystems are considered as a complex process because they are underwater features. Remote sensing can be quite useful in this by providing huge amount of database of satellite images. Landsat series have been providing satellite data for last 50 years and now it is one of the large databases providing satellite imageries. Accurate processing of these images can increase the accuracy of coral reef information extraction from these images. Classification is one of the important processes that can give adequate and precise information about coral reefs. This study is intended to compare various supervised classification techniques available for extraction of information from the image and to suggest best of them. Maximum Likelihood and Support Vector Machine have proven to be good classification module that can be used to classify Landsat Images to coral reef detection and monitoring.