Dr. Ayman Nabil
Eng. Youmna Hassan
Senior Teaching Assistant
Eng. Maha Sayed
The high demand of oil caused a huge loss in form of oil spills during the process of exporting the product, which lead to an increase in the pollution specially the marine environment. Our project maintain this problem using modern technology by detecting Oil spills using satellite imagery. This detection is achieved with the assistance of various machine learning technologies and image processing methods using satellite hyperspectral images. The geologist team had defined hyperspectral images as the principle feature which focus on detecting the oil spills during all the process during the exporting process. The system uses this spectrum and special feature that will be extracted from images using some filters and classifiers. we perform preprocessing on the image, going through the process then using various classifiers to accomplish the best accuracy. The study area was decided based on the huge amount of oil detected in such as Gulf Of Mexico which is obtained using AVIRIS dataset.
• To research on the most effective way to detect oil spills.
• To minimize damage done during the exporting process to the marine environment.
• Use the image processing techniques to enhance the hyperspectral image.
• Use machine learning techniques to help improve the detecting methods that are used and obtaining results.
• To evaluate the obtained results by comparing it with the previous work results.
• Providing detection system for possible oil spills locations.
• Obtaining the oil spill feature from the hyperspectral images is by using pixel analysis from each band.
• Clustering the data by the feature extraction analysis.
• Providing different classifiers to obtain the accuracy result needed for the research process.
You will find here the documents and presentation for our proposal.
You will find here the documents and presentation for our SRS.
You will find here the documents and presentation for our SDD.
You will find here the documents and presentation for our Thesis